Acoustic diversity indices as biodiversity indicators: a meta-analysis

Supplementary material

---

    require(knitr)
    require(png)
    require(dplyr)
    require(stringr)
    require(metafor)
    require(compute.es)
    require(MuMIn); eval(metafor:::.MuMIn)
    require(kableExtra)
    require(pander)
    require(ggplot2)
    require(RColorBrewer)
  
    source("src/acoustic_indices_functions.R")

This page describes the systematic review procedure and meta-analytic steps we took while assessing if acoustic indices are reliable indicators of biodiversity.

Systematic review

We extensively searched existing literature for studies assessing the use of acoustic indices as proxies of biodiversity. The systematic search proceeded as follows:

1. We compiled studies that used acoustic diversity indices from four recent literature reviews on acoustic indices, biodiversity assessment and passive acoustic monitoring (i.e. (Sueur et al., 2014; Buxton et al., 2018a; Buxton et al., 2018b; Sugai et al., 2019)).
2. We updated the literature database from 2017 up to July 2019 with Thompson’s ISI Web of Science (WoS), querying the database for the keywords (i.e. bioacoustic∗ AND ind∗, ecoacoustic∗, acoustic∗ AND biodiversity, soundscape AND ecology). This search was restricted to 9 WoS subject areas (i.e., zoology, environmental sciences ecology, behavioural sciences, biodiversity conservation, marine freshwater biology, acoustics, evolutionary biology, entomology, and remote sensing).
3. Additionally, we used Google Scholar, to compile all literature from 2017 to July 2019 that cited the papers originally describing the eleven selected indices as well as literature that cited the recent papers published within this period.

Both peer-reviewed and no peer-reviewed studies were included to avoid publication bias.

After removing all duplicates, we gathered a total of 1,039 studies.

Inclusion criteria

We considered studies eligible for meta-analysis if they met the following inclusion criteria:

• The study reported data to test the efficiency of acoustic diversity indices as indicators of biodiversity.
• The study provided statistical or graphical information of the direct relationship between biological diversity and acoustic diversity.
• We only considered studies using the 11 most common alpha acoustic indices (ACI, ADI, AEI, AR, BIO, ,H, Hf, Ht, M, NDSI, NP).

Following such inclusion criteria, we screened all selected studies (n = 1,039) based on their abstract, titles and keywords and thereby retaining 142 studies that were identified as potentially eligible. To ascertain their relevance, we conducted a full-text assessment on all these studies, finally retaining 35 studies that passed through all the criteria.

knitr::include_graphics("rmd/Fig.S1.png")

Figure S1 - Temporal evolution (2007 – 2019) of the validation data from the total of 142 articles. Articles which correlate the acoustic indices with real biological data are represented with an orange line and studies which do not correlate with a green line

Data extraction

Main extracted data

For each study:

• We retrieved the acoustic indices calculated and related to biological diversity. The selected indices were: ACI, ADI, AEI, AR, BIO, H, Hf, Ht, M, NDSI, NP.
  
• We classified the diversity estimates that were measured and related to acoustic indices into five types: Abundance - number of individuals of a single or several species; Species richness - number of vocal and/or non-vocal species; Species diversity - Shannon index, Simpson index or species evenness; Sound abundance - number of sounds by known or unknown species.; Sound richness - number of sounds types by known or unknown species.
  
• We described the method applied to obtain such biological information based on two variables: Acoustic - data extracted from audio recordings; Non-acoustic - from sources other than recordings, i.e. literature, field surveys, etc.
  
• We included the environment type: Terrestrial; Aquatic.
• And studied taxonomic groups: Invertebrates; Anurans; Fish; Birds; Mammals; Several.

Handling of pseudoreplication

To account for differences in sampling effort among studies and to support the detection of cases of pseudoreplication that might potentially lead to biased statistical tests, we extensively assessed the study design by identifying a total of ten features that summarized both spatial and temporal sampling of each eligible study. When a mismatch between sample size and the number of true replicates was identified, we classified the analysis as using inflated replication and used true replicates as our sample size for meta-analysis.

Effect size calculation

We used Pearson’s correlation coefficient r, as our measure of effect size. The effect size describes the magnitude of the relationship between acoustic indices and biodiversity. A positive correlation indicates a positive relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to higher biodiversity) whereas a negative correlation indicates a negative relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to lower biodiversity).

When Pearson’s correlation, r, could not be directly collected from the studies, we extracted other statistics, such as Spearman’s correlation, t-values, F-values, linear regression slope coefficients and R². If only graphical information was available we extracted the statistics with Web Plot Digitizer v4.2. (Rohatgi, 2019). We converted all statistics to r, using compute.es package in R (Del Re & Del Re, 2012) or when the package did not provide the needed functions we followed the formulas provided in Nakagawa & Cuthill (2007) and Koricheva, Gurevitch, & Mengersen (2013). Whenever study information was insufficient to compute the effect size, we contacted corresponding authors for missing data.

We converted our effect size r to Fisher’s Z in order to satisfy the normality assumption of parametric meta-analysis (Nakagawa & Cuthill, 2007). Fisher’s Z values were converted back to r, to ease interpretation of results.

Dataset

We collected a total of 481 effect sizes from 35 studies. These number was reduced to 364 effect sizes and 34 studies, after computing composite effect sizes between non-independent effect sizes and removing a study due to difficulty in describing the study design.

    df_raw <- read.csv("data/Table.S1.csv")
    n_used <- "n_adjusted"
    # Use n_adjusted as sample size
    df_tidy <- tidy_data(df_raw, n_used)

## Removed study id
##   54 
## Dataframe aggregated from 481 to 364 entries

    # Studies database
    studies <- read.csv("data/Table.S2.csv")
    df_tidy <- merge(df_tidy, studies, by.x = "id", by.y = "ID", all.x = TRUE)
    df_tidy <- df_tidy %>% 
               mutate(authors = paste(Authors, year)) %>%
               select(id, entry, authors, everything(), -Publ_year, -Title,
                      -doi, -Authors) 

Table S1 - Complete dataset used in meta-analysis.

kable(df_tidy, format = "html") %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
     scroll_box(height = "400px", width = "100%")

id	entry	authors	year	impact_factor	index	taxa	environ	bio	diversity_source	pseudoreplication	n	z	var	Journal
2	15	Desjonquères et al. 2015	2015	2.180	ACI	invertebrates	A	sound_abundance	acoustic	YES	4	0.3285000	0.7500000	PeerJ
2	16	Desjonquères et al. 2015	2015	2.180	ACI	invertebrates	A	sound_richness	acoustic	YES	4	0.3322500	0.7500000	PeerJ
2	17	Desjonquères et al. 2015	2015	2.180	AR	invertebrates	A	sound_abundance	acoustic	YES	4	0.0692000	0.7500000	PeerJ
2	18	Desjonquères et al. 2015	2015	2.180	AR	invertebrates	A	sound_richness	acoustic	YES	4	0.0851500	0.7500000	PeerJ
2	19	Desjonquères et al. 2015	2015	2.180	Hf	invertebrates	A	sound_abundance	acoustic	YES	4	-0.1851000	0.7500000	PeerJ
2	20	Desjonquères et al. 2015	2015	2.180	Hf	invertebrates	A	sound_richness	acoustic	YES	4	-0.1304000	0.7500000	PeerJ
2	21	Desjonquères et al. 2015	2015	2.180	Ht	invertebrates	A	sound_abundance	acoustic	YES	4	-0.3286500	0.7500000	PeerJ
2	22	Desjonquères et al. 2015	2015	2.180	Ht	invertebrates	A	sound_richness	acoustic	YES	4	-0.2970000	0.7500000	PeerJ
2	23	Desjonquères et al. 2015	2015	2.180	M	invertebrates	A	sound_abundance	acoustic	YES	4	0.3453000	0.7500000	PeerJ
2	24	Desjonquères et al. 2015	2015	2.180	M	invertebrates	A	sound_richness	acoustic	YES	4	0.3043500	0.7500000	PeerJ
2	25	Desjonquères et al. 2015	2015	2.180	NP	invertebrates	A	sound_abundance	acoustic	YES	4	0.2074500	0.7500000	PeerJ
2	26	Desjonquères et al. 2015	2015	2.180	NP	invertebrates	A	sound_richness	acoustic	YES	4	0.1851000	0.7500000	PeerJ
4	13	Parks et al. 2014	2014	1.730	H	mammals	A	sound_abundance	acoustic	YES	4	0.2801000	0.7500000	Ecol. Inform.
6	1	Boelman et al. 2007	2007	3.570	BIO	birds	T	abundance	no_acoustic	NO	8	1.5047000	0.2000000	Ecol. Apli.
9	44	Harris et al. 2016	2016	5.710	ACI	fish	A	diversity	no_acoustic	NO	9	1.0278500	0.1250250	Methods Ecol. Evol.
9	45	Harris et al. 2016	2016	5.710	ACI	fish	A	richness	no_acoustic	NO	9	0.2877000	0.1667000	Methods Ecol. Evol.
9	46	Harris et al. 2016	2016	5.710	AR	fish	A	diversity	no_acoustic	NO	9	0.2104000	0.1250250	Methods Ecol. Evol.
9	47	Harris et al. 2016	2016	5.710	AR	fish	A	richness	no_acoustic	NO	9	0.5361000	0.1667000	Methods Ecol. Evol.
9	48	Harris et al. 2016	2016	5.710	H	fish	A	diversity	no_acoustic	NO	9	0.3258625	0.0937688	Methods Ecol. Evol.
9	49	Harris et al. 2016	2016	5.710	H	fish	A	richness	no_acoustic	NO	9	0.6448500	0.1041875	Methods Ecol. Evol.
10	38	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.1003000	0.0303000	Sci Rep
10	39	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.0000000	0.0303000	Sci Rep
10	170	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.8291000	0.0303000	Sci Rep
10	171	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.1717000	0.0303000	Sci Rep
10	228	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	1.2562000	0.0303000	Sci Rep
10	229	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.2132000	0.0303000	Sci Rep
10	276	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.9076000	0.0303000	Sci Rep
10	277	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.1923000	0.0303000	Sci Rep
10	300	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.1206000	0.0303000	Sci Rep
10	301	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.2554000	0.0303000	Sci Rep
10	311	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.0500000	0.0303000	Sci Rep
10	312	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.2769000	0.0303000	Sci Rep
10	326	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.3428000	0.0303000	Sci Rep
10	327	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.8673000	0.0303000	Sci Rep
10	339	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.3205000	0.0303000	Sci Rep
10	340	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	1.0454000	0.0303000	Sci Rep
10	350	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.3769000	0.0303000	Sci Rep
10	351	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	1.0203000	0.0303000	Sci Rep
10	361	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.2237000	0.0303000	Sci Rep
10	362	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.9287000	0.0303000	Sci Rep
10	363	Buscaino et al. 2016	2016	4.259	ACI	fish	A	sound_abundance	acoustic	NO	36	0.3428000	0.0303000	Sci Rep
10	364	Buscaino et al. 2016	2016	4.259	ACI	invertebrates	A	sound_abundance	acoustic	NO	36	0.7582000	0.0303000	Sci Rep
11	40	Bertucci et al. 2016	2016	4.260	ACI	fish	A	abundance	no_acoustic	NO	8	-0.1262000	0.1500000	Sci Rep
11	41	Bertucci et al. 2016	2016	4.260	ACI	fish	A	diversity	no_acoustic	NO	8	1.0328000	0.2000000	Sci Rep
11	42	Bertucci et al. 2016	2016	4.260	ACI	fish	A	richness	no_acoustic	NO	8	0.8821000	0.1500000	Sci Rep
11	172	Bertucci et al. 2016	2016	4.260	ACI	fish	A	abundance	no_acoustic	NO	8	0.3009000	0.1500000	Sci Rep
11	173	Bertucci et al. 2016	2016	4.260	ACI	fish	A	diversity	no_acoustic	NO	8	0.4181000	0.2000000	Sci Rep
11	174	Bertucci et al. 2016	2016	4.260	ACI	fish	A	richness	no_acoustic	NO	8	1.1245500	0.1500000	Sci Rep
11	230	Bertucci et al. 2016	2016	4.260	ACI	fish	A	abundance	no_acoustic	NO	8	-0.1797000	0.1500000	Sci Rep
11	231	Bertucci et al. 2016	2016	4.260	ACI	fish	A	diversity	no_acoustic	NO	8	0.8429000	0.2000000	Sci Rep
11	232	Bertucci et al. 2016	2016	4.260	ACI	fish	A	richness	no_acoustic	NO	8	0.6372500	0.1500000	Sci Rep
11	278	Bertucci et al. 2016	2016	4.260	ACI	fish	A	abundance	no_acoustic	NO	8	-0.0471500	0.1500000	Sci Rep
11	279	Bertucci et al. 2016	2016	4.260	ACI	fish	A	diversity	no_acoustic	NO	8	0.6747000	0.2000000	Sci Rep
11	280	Bertucci et al. 2016	2016	4.260	ACI	fish	A	richness	no_acoustic	NO	8	0.6980000	0.1500000	Sci Rep
13	11	McWilliam & Hawkin 2013	2013	2.480	ACI	invertebrates	A	sound_abundance	acoustic	YES	5	1.1881000	0.5000000	J. ExpMar. Biol. Ecol
13	12	McWilliam & Hawkin 2013	2013	2.480	ADI	invertebrates	A	sound_abundance	acoustic	YES	5	1.3331000	0.5000000	J. ExpMar. Biol. Ecol
14	34	Wa Maina et al. 2016	2016	1.220	ACI	birds	T	richness	acoustic	NO	8	0.1689000	0.2000000	BDJ
14	35	Wa Maina et al. 2016	2016	1.220	ACI	birds	T	richness	no_acoustic	NO	8	0.9638000	0.2000000	BDJ
14	36	Wa Maina et al. 2016	2016	1.220	H	birds	T	richness	acoustic	NO	8	0.4676000	0.2000000	BDJ
14	37	Wa Maina et al. 2016	2016	1.220	H	birds	T	richness	no_acoustic	NO	8	0.5870000	0.2000000	BDJ
15	43	Roca & Proulx 2016	2016	4.810	H	invertebrates	T	richness	acoustic	NO	4	1.8972000	1.0000000	Ecology
15	175	Roca & Proulx 2016	2016	4.810	H	invertebrates	T	richness	acoustic	NO	4	2.6467000	1.0000000	Ecology
15	233	Roca & Proulx 2016	2016	4.810	H	invertebrates	T	richness	acoustic	NO	4	2.3796000	1.0000000	Ecology
17	14	Zhang et al. 2015	2015	0.000	ACI	birds	T	richness	acoustic	YES	4	0.3940000	1.0000000	IEEE.Conference.procedings
37	33	Picciulin et al. 2016	2016	0.000	ACI	fish	A	sound_abundance	acoustic	YES	4	0.3260000	1.0000000	Proceedings of Meetings on Acoustics
41	3	Paisley-Jones 2011	2011	0.000	H	birds	T	sound_abundance	acoustic	NO	6	0.1481000	0.3333000	thesis
41	4	Paisley-Jones 2011	2011	0.000	H	invertebrates	T	diversity	no_acoustic	NO	6	-0.1430000	0.3333000	thesis
44	86	Machado et al. 2017	2017	4.994	ADI	birds	T	richness	acoustic	NO	30	0.4910000	0.0370000	Landsc. Urban Plan.
44	87	Machado et al. 2017	2017	4.994	NDSI	birds	T	richness	acoustic	NO	30	0.1686000	0.0370000	Landsc. Urban Plan.
45	7	McLaren 2012	2012	0.000	NDSI	birds	T	diversity	no_acoustic	NO	36	0.9330000	0.0303000	practicum
45	8	McLaren 2012	2012	0.000	NDSI	birds	T	richness	acoustic	NO	36	0.6070000	0.0303000	practicum
45	9	McLaren 2012	2012	0.000	NDSI	birds	T	richness	no_acoustic	NO	36	0.9160000	0.0303000	practicum
53	154	Moreno-Gomez 2019	2019	4.490	ACI	anurans	T	richness	acoustic	NO	33	0.0000000	0.0333000	Ecol. Indic.
53	155	Moreno-Gomez 2019	2019	4.490	ACI	birds	T	richness	acoustic	NO	33	-0.0209000	0.0333000	Ecol. Indic.
53	156	Moreno-Gomez 2019	2019	4.490	ADI	anurans	T	richness	acoustic	NO	33	0.1051000	0.0333000	Ecol. Indic.
53	157	Moreno-Gomez 2019	2019	4.490	ADI	birds	T	richness	acoustic	NO	33	-0.3237000	0.0333000	Ecol. Indic.
53	158	Moreno-Gomez 2019	2019	4.490	AEI	anurans	T	richness	acoustic	NO	33	-0.2122000	0.0333000	Ecol. Indic.
53	159	Moreno-Gomez 2019	2019	4.490	AEI	birds	T	richness	acoustic	NO	33	0.3237000	0.0333000	Ecol. Indic.
53	160	Moreno-Gomez 2019	2019	4.490	BIO	anurans	T	richness	acoustic	NO	33	0.1051000	0.0333000	Ecol. Indic.
53	161	Moreno-Gomez 2019	2019	4.490	BIO	birds	T	richness	acoustic	NO	33	-0.1051000	0.0333000	Ecol. Indic.
53	162	Moreno-Gomez 2019	2019	4.490	H	anurans	T	richness	acoustic	NO	33	0.1051000	0.0333000	Ecol. Indic.
53	163	Moreno-Gomez 2019	2019	4.490	H	birds	T	richness	acoustic	NO	33	-0.1051000	0.0333000	Ecol. Indic.
53	164	Moreno-Gomez 2019	2019	4.490	Hf	anurans	T	richness	acoustic	NO	33	-0.1051000	0.0333000	Ecol. Indic.
53	165	Moreno-Gomez 2019	2019	4.490	Hf	birds	T	richness	acoustic	NO	33	0.1051000	0.0333000	Ecol. Indic.
53	166	Moreno-Gomez 2019	2019	4.490	Ht	anurans	T	richness	acoustic	NO	33	0.1051000	0.0333000	Ecol. Indic.
53	167	Moreno-Gomez 2019	2019	4.490	Ht	birds	T	richness	acoustic	NO	33	-0.3237000	0.0333000	Ecol. Indic.
53	214	Moreno-Gomez 2019	2019	4.490	ACI	anurans	T	richness	acoustic	NO	11	-0.1051000	0.1250000	Ecol. Indic.
53	215	Moreno-Gomez 2019	2019	4.490	ACI	birds	T	richness	acoustic	NO	11	0.3237000	0.1250000	Ecol. Indic.
53	216	Moreno-Gomez 2019	2019	4.490	ADI	anurans	T	richness	acoustic	NO	11	0.1051000	0.1250000	Ecol. Indic.
53	217	Moreno-Gomez 2019	2019	4.490	ADI	birds	T	richness	acoustic	NO	11	-0.2122000	0.1250000	Ecol. Indic.
53	218	Moreno-Gomez 2019	2019	4.490	AEI	anurans	T	richness	acoustic	NO	11	-0.1051000	0.1250000	Ecol. Indic.
53	219	Moreno-Gomez 2019	2019	4.490	AEI	birds	T	richness	acoustic	NO	11	0.3237000	0.1250000	Ecol. Indic.
53	220	Moreno-Gomez 2019	2019	4.490	BIO	anurans	T	richness	acoustic	NO	11	-0.1051000	0.1250000	Ecol. Indic.
53	221	Moreno-Gomez 2019	2019	4.490	BIO	birds	T	richness	acoustic	NO	11	0.2122000	0.1250000	Ecol. Indic.
53	222	Moreno-Gomez 2019	2019	4.490	H	anurans	T	richness	acoustic	NO	11	0.1051000	0.1250000	Ecol. Indic.
53	223	Moreno-Gomez 2019	2019	4.490	H	birds	T	richness	acoustic	NO	11	-0.3237000	0.1250000	Ecol. Indic.
53	224	Moreno-Gomez 2019	2019	4.490	Hf	anurans	T	richness	acoustic	NO	11	-0.0105000	0.1250000	Ecol. Indic.
53	225	Moreno-Gomez 2019	2019	4.490	Hf	birds	T	richness	acoustic	NO	11	-0.1051000	0.1250000	Ecol. Indic.
53	226	Moreno-Gomez 2019	2019	4.490	Ht	anurans	T	richness	acoustic	NO	11	0.1051000	0.1250000	Ecol. Indic.
53	227	Moreno-Gomez 2019	2019	4.490	Ht	birds	T	richness	acoustic	NO	11	-0.4426000	0.1250000	Ecol. Indic.
53	262	Moreno-Gomez 2019	2019	4.490	ACI	anurans	T	richness	acoustic	NO	32	0.1051000	0.0345000	Ecol. Indic.
53	263	Moreno-Gomez 2019	2019	4.490	ACI	birds	T	richness	acoustic	NO	32	0.5731000	0.0345000	Ecol. Indic.
53	264	Moreno-Gomez 2019	2019	4.490	ADI	anurans	T	richness	acoustic	NO	32	0.1051000	0.0345000	Ecol. Indic.
53	265	Moreno-Gomez 2019	2019	4.490	ADI	birds	T	richness	acoustic	NO	32	-0.1051000	0.0345000	Ecol. Indic.
53	266	Moreno-Gomez 2019	2019	4.490	AEI	anurans	T	richness	acoustic	NO	32	-0.1051000	0.0345000	Ecol. Indic.
53	267	Moreno-Gomez 2019	2019	4.490	AEI	birds	T	richness	acoustic	NO	32	0.2122000	0.0345000	Ecol. Indic.
53	268	Moreno-Gomez 2019	2019	4.490	BIO	anurans	T	richness	acoustic	NO	32	-0.2122000	0.0345000	Ecol. Indic.
53	269	Moreno-Gomez 2019	2019	4.490	BIO	birds	T	richness	acoustic	NO	32	0.3237000	0.0345000	Ecol. Indic.
53	270	Moreno-Gomez 2019	2019	4.490	H	anurans	T	richness	acoustic	NO	32	0.0105000	0.0345000	Ecol. Indic.
53	271	Moreno-Gomez 2019	2019	4.490	H	birds	T	richness	acoustic	NO	32	0.3237000	0.0345000	Ecol. Indic.
53	272	Moreno-Gomez 2019	2019	4.490	Hf	anurans	T	richness	acoustic	NO	32	0.0000000	0.0345000	Ecol. Indic.
53	273	Moreno-Gomez 2019	2019	4.490	Hf	birds	T	richness	acoustic	NO	32	0.3237000	0.0345000	Ecol. Indic.
53	274	Moreno-Gomez 2019	2019	4.490	Ht	anurans	T	richness	acoustic	NO	32	0.0105000	0.0345000	Ecol. Indic.
53	275	Moreno-Gomez 2019	2019	4.490	Ht	birds	T	richness	acoustic	NO	32	-0.4426000	0.0345000	Ecol. Indic.
60	152	Patrick Lyon et al. 2019	2019	2.360	ACI	fish	A	abundance	no_acoustic	NO	7	-0.3654000	0.2500000	Mar. Ecol.-Prog. Ser.
60	153	Patrick Lyon et al. 2019	2019	2.360	ACI	fish	A	diversity	no_acoustic	NO	7	0.1306000	0.1875000	Mar. Ecol.-Prog. Ser.
60	212	Patrick Lyon et al. 2019	2019	2.360	ACI	fish	A	abundance	no_acoustic	NO	7	0.5763000	0.2500000	Mar. Ecol.-Prog. Ser.
60	213	Patrick Lyon et al. 2019	2019	2.360	ACI	fish	A	diversity	no_acoustic	NO	7	0.4313500	0.1875000	Mar. Ecol.-Prog. Ser.
70	94	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	NO	9	-0.2232000	0.1250250	Sci Rep
70	95	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_richness	acoustic	NO	9	0.6011000	0.1667000	Sci Rep
70	199	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	NO	4	0.0262000	1.0000000	Sci Rep
70	200	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	NO	9	0.6011000	0.1667000	Sci Rep
70	201	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	NO	10	0.4676000	0.1429000	Sci Rep
70	202	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_richness	acoustic	NO	9	0.1051000	0.1667000	Sci Rep
70	203	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_richness	acoustic	NO	10	0.4931000	0.1429000	Sci Rep
70	256	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	NO	9	0.8244000	0.1667000	Sci Rep
70	257	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_richness	acoustic	NO	9	1.2973000	0.1667000	Sci Rep
70	294	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_abundance	acoustic	YES	25	0.3422714	0.0260000	Sci Rep
70	295	Bolgan et al. 2018	2018	4.010	ACI	fish	A	sound_richness	acoustic	YES	25	0.2930143	0.0260000	Sci Rep
77	71	Fairbrass et al. 2017	2017	3.980	ACI	several	T	richness	acoustic	NO	105	0.8513000	0.0099000	Ecol. Indic.
77	80	Fairbrass et al. 2017	2017	3.980	BIO	several	T	richness	acoustic	NO	105	0.4545000	0.0099000	Ecol. Indic.
77	85	Fairbrass et al. 2017	2017	3.980	NDSI	several	T	richness	acoustic	NO	105	0.4320000	0.0099000	Ecol. Indic.
77	186	Fairbrass et al. 2017	2017	3.980	ADI	several	T	richness	acoustic	NO	105	0.1968000	0.0099000	Ecol. Indic.
80	62	Staaterman et al. 2017	2017	2.280	ACI	fish	A	abundance	no_acoustic	NO	4	-0.1203000	0.6666667	Mar. Ecol.-Prog. Ser.
80	63	Staaterman et al. 2017	2017	2.280	ACI	fish	A	diversity	no_acoustic	NO	4	0.0000000	0.7500000	Mar. Ecol.-Prog. Ser.
80	64	Staaterman et al. 2017	2017	2.280	ACI	fish	A	richness	no_acoustic	NO	4	-0.2158000	0.6666667	Mar. Ecol.-Prog. Ser.
80	65	Staaterman et al. 2017	2017	2.280	H	fish	A	abundance	no_acoustic	NO	4	0.4060667	0.6666667	Mar. Ecol.-Prog. Ser.
80	66	Staaterman et al. 2017	2017	2.280	H	fish	A	diversity	no_acoustic	NO	4	-0.1586500	0.7500000	Mar. Ecol.-Prog. Ser.
80	67	Staaterman et al. 2017	2017	2.280	H	fish	A	richness	no_acoustic	NO	4	0.3178667	0.6666667	Mar. Ecol.-Prog. Ser.
80	176	Staaterman et al. 2017	2017	2.280	ACI	fish	A	abundance	no_acoustic	NO	4	0.0335667	0.6666667	Mar. Ecol.-Prog. Ser.
80	177	Staaterman et al. 2017	2017	2.280	ACI	fish	A	diversity	no_acoustic	NO	4	0.1990500	0.7500000	Mar. Ecol.-Prog. Ser.
80	178	Staaterman et al. 2017	2017	2.280	ACI	fish	A	richness	no_acoustic	NO	4	0.0357000	0.6666667	Mar. Ecol.-Prog. Ser.
80	179	Staaterman et al. 2017	2017	2.280	H	fish	A	abundance	no_acoustic	NO	4	0.4564000	0.6666667	Mar. Ecol.-Prog. Ser.
80	180	Staaterman et al. 2017	2017	2.280	H	fish	A	diversity	no_acoustic	NO	4	-0.2144000	0.7500000	Mar. Ecol.-Prog. Ser.
80	181	Staaterman et al. 2017	2017	2.280	H	fish	A	richness	no_acoustic	NO	4	0.4316333	0.6666667	Mar. Ecol.-Prog. Ser.
80	234	Staaterman et al. 2017	2017	2.280	ACI	fish	A	abundance	no_acoustic	NO	4	0.0357000	0.6666667	Mar. Ecol.-Prog. Ser.
80	235	Staaterman et al. 2017	2017	2.280	ACI	fish	A	diversity	no_acoustic	NO	4	-0.2144000	0.7500000	Mar. Ecol.-Prog. Ser.
80	236	Staaterman et al. 2017	2017	2.280	ACI	fish	A	richness	no_acoustic	NO	4	-0.3736000	0.6666667	Mar. Ecol.-Prog. Ser.
80	237	Staaterman et al. 2017	2017	2.280	H	fish	A	abundance	no_acoustic	NO	4	0.2231333	0.6666667	Mar. Ecol.-Prog. Ser.
80	238	Staaterman et al. 2017	2017	2.280	H	fish	A	diversity	no_acoustic	NO	4	0.1093000	0.7500000	Mar. Ecol.-Prog. Ser.
80	239	Staaterman et al. 2017	2017	2.280	H	fish	A	richness	no_acoustic	NO	4	0.0417667	0.6666667	Mar. Ecol.-Prog. Ser.
80	281	Staaterman et al. 2017	2017	2.280	ACI	fish	A	abundance	no_acoustic	NO	4	0.0357000	0.6666667	Mar. Ecol.-Prog. Ser.
80	282	Staaterman et al. 2017	2017	2.280	ACI	fish	A	diversity	no_acoustic	NO	4	-0.1586500	0.7500000	Mar. Ecol.-Prog. Ser.
80	283	Staaterman et al. 2017	2017	2.280	ACI	fish	A	richness	no_acoustic	NO	4	-0.3385667	0.6666667	Mar. Ecol.-Prog. Ser.
80	284	Staaterman et al. 2017	2017	2.280	H	fish	A	abundance	no_acoustic	NO	4	0.2231333	0.6666667	Mar. Ecol.-Prog. Ser.
80	285	Staaterman et al. 2017	2017	2.280	H	fish	A	diversity	no_acoustic	NO	4	-0.1061000	0.7500000	Mar. Ecol.-Prog. Ser.
80	286	Staaterman et al. 2017	2017	2.280	H	fish	A	richness	no_acoustic	NO	4	0.2055667	0.6666667	Mar. Ecol.-Prog. Ser.
86	92	Indraswari et al. 2018	2018	3.400	ACI	anurans	T	sound_abundance	acoustic	YES	33	0.6284000	0.0333000	Freshw. Biol.
86	93	Indraswari et al. 2018	2018	3.400	Ht	anurans	T	sound_abundance	acoustic	YES	33	0.6948000	0.0333000	Freshw. Biol.
86	197	Indraswari et al. 2018	2018	3.400	ACI	anurans	T	sound_abundance	acoustic	YES	33	0.3773000	0.0333000	Freshw. Biol.
86	198	Indraswari et al. 2018	2018	3.400	Ht	anurans	T	sound_abundance	acoustic	YES	33	0.4426000	0.0333000	Freshw. Biol.
86	254	Indraswari et al. 2018	2018	3.400	ACI	anurans	T	sound_abundance	acoustic	YES	33	0.2617000	0.0333000	Freshw. Biol.
86	255	Indraswari et al. 2018	2018	3.400	Ht	anurans	T	sound_abundance	acoustic	YES	33	0.4880000	0.0333000	Freshw. Biol.
87	96	Eldridge et al. 2018	2018	4.490	ACI	birds	T	richness	acoustic	YES	4	0.6250000	0.6000000	Ecol. Indic.
87	98	Eldridge et al. 2018	2018	4.490	ACI	birds	T	sound_abundance	acoustic	YES	4	0.6507600	0.6000000	Ecol. Indic.
87	204	Eldridge et al. 2018	2018	4.490	ACI	birds	T	richness	acoustic	YES	4	0.1909000	0.6000000	Ecol. Indic.
87	206	Eldridge et al. 2018	2018	4.490	ACI	several	T	sound_abundance	acoustic	YES	4	-0.0819200	0.6000000	Ecol. Indic.
87	258	Eldridge et al. 2018	2018	4.490	ADI	birds	T	richness	acoustic	YES	4	-0.7218000	1.0000000	Ecol. Indic.
87	259	Eldridge et al. 2018	2018	4.490	ADI	birds	T	sound_abundance	acoustic	YES	4	-0.7547000	1.0000000	Ecol. Indic.
87	260	Eldridge et al. 2018	2018	4.490	AEI	birds	T	richness	acoustic	YES	4	0.5061000	1.0000000	Ecol. Indic.
87	261	Eldridge et al. 2018	2018	4.490	AEI	birds	T	sound_abundance	acoustic	YES	4	0.8429000	1.0000000	Ecol. Indic.
87	296	Eldridge et al. 2018	2018	4.490	ADI	birds	T	richness	acoustic	YES	4	-0.2078000	1.0000000	Ecol. Indic.
87	297	Eldridge et al. 2018	2018	4.490	ADI	several	T	sound_abundance	acoustic	YES	4	-0.5061000	1.0000000	Ecol. Indic.
87	298	Eldridge et al. 2018	2018	4.490	AEI	birds	T	richness	acoustic	YES	4	0.2100000	1.0000000	Ecol. Indic.
87	299	Eldridge et al. 2018	2018	4.490	AEI	several	T	sound_abundance	acoustic	YES	4	0.5731000	1.0000000	Ecol. Indic.
87	309	Eldridge et al. 2018	2018	4.490	BIO	birds	T	richness	acoustic	YES	4	0.8064000	1.0000000	Ecol. Indic.
87	310	Eldridge et al. 2018	2018	4.490	BIO	birds	T	sound_abundance	acoustic	YES	4	0.9009000	1.0000000	Ecol. Indic.
87	320	Eldridge et al. 2018	2018	4.490	BIO	birds	T	richness	acoustic	YES	4	0.2100000	1.0000000	Ecol. Indic.
87	321	Eldridge et al. 2018	2018	4.490	BIO	several	T	sound_abundance	acoustic	YES	4	0.6446000	1.0000000	Ecol. Indic.
87	322	Eldridge et al. 2018	2018	4.490	Hf	birds	T	richness	acoustic	YES	4	-0.5731000	1.0000000	Ecol. Indic.
87	323	Eldridge et al. 2018	2018	4.490	Hf	birds	T	sound_abundance	acoustic	YES	4	-0.7218000	1.0000000	Ecol. Indic.
87	324	Eldridge et al. 2018	2018	4.490	Ht	birds	T	richness	acoustic	YES	4	-0.6595000	1.0000000	Ecol. Indic.
87	325	Eldridge et al. 2018	2018	4.490	Ht	birds	T	sound_abundance	acoustic	YES	4	-0.6902000	1.0000000	Ecol. Indic.
87	335	Eldridge et al. 2018	2018	4.490	Hf	birds	T	richness	acoustic	YES	4	-0.1905000	1.0000000	Ecol. Indic.
87	336	Eldridge et al. 2018	2018	4.490	Hf	several	T	sound_abundance	acoustic	YES	4	-0.6446000	1.0000000	Ecol. Indic.
87	337	Eldridge et al. 2018	2018	4.490	Ht	birds	T	richness	acoustic	YES	4	-0.1905000	1.0000000	Ecol. Indic.
87	338	Eldridge et al. 2018	2018	4.490	Ht	several	T	sound_abundance	acoustic	YES	4	-0.3237000	1.0000000	Ecol. Indic.
87	348	Eldridge et al. 2018	2018	4.490	NDSI	birds	T	richness	acoustic	YES	4	0.3237000	1.0000000	Ecol. Indic.
87	349	Eldridge et al. 2018	2018	4.490	NDSI	birds	T	sound_abundance	acoustic	YES	4	0.4303000	1.0000000	Ecol. Indic.
87	359	Eldridge et al. 2018	2018	4.490	NDSI	birds	T	richness	acoustic	YES	4	0.2672000	1.0000000	Ecol. Indic.
87	360	Eldridge et al. 2018	2018	4.490	NDSI	several	T	sound_abundance	acoustic	YES	4	0.4303000	1.0000000	Ecol. Indic.
89	50	Gage et al. 2017	2017	1.820	ACI	birds	T	richness	acoustic	YES	60	0.6885000	0.0175000	Ecol. Inform.
89	51	Gage et al. 2017	2017	1.820	ACI	birds	T	sound_abundance	acoustic	YES	60	1.4618000	0.0175000	Ecol. Inform.
89	52	Gage et al. 2017	2017	1.820	ADI	birds	T	richness	acoustic	YES	60	-1.8635000	0.0175000	Ecol. Inform.
89	53	Gage et al. 2017	2017	1.820	ADI	birds	T	sound_abundance	acoustic	YES	60	-0.8852000	0.0175000	Ecol. Inform.
89	54	Gage et al. 2017	2017	1.820	AEI	birds	T	richness	acoustic	YES	60	2.2494000	0.0175000	Ecol. Inform.
89	55	Gage et al. 2017	2017	1.820	AEI	birds	T	sound_abundance	acoustic	YES	60	1.0302000	0.0175000	Ecol. Inform.
89	56	Gage et al. 2017	2017	1.820	BIO	birds	T	richness	acoustic	YES	60	-0.6098000	0.0175000	Ecol. Inform.
89	57	Gage et al. 2017	2017	1.820	BIO	birds	T	sound_abundance	acoustic	YES	60	-0.0993000	0.0175000	Ecol. Inform.
89	58	Gage et al. 2017	2017	1.820	H	birds	T	richness	acoustic	YES	60	1.0849000	0.0175000	Ecol. Inform.
89	59	Gage et al. 2017	2017	1.820	H	birds	T	sound_abundance	acoustic	YES	60	2.4427000	0.0175000	Ecol. Inform.
89	60	Gage et al. 2017	2017	1.820	NDSI	birds	T	richness	acoustic	YES	60	0.0270000	0.0175000	Ecol. Inform.
89	61	Gage et al. 2017	2017	1.820	NDSI	birds	T	sound_abundance	acoustic	YES	60	0.5269000	0.0175000	Ecol. Inform.
90	104	Ferreira et al. 2018	2018	NA	ACI	anurans	T	sound_richness	acoustic	NO	7	0.0556000	0.2500000	Journal of ecoacoustics
90	108	Ferreira et al. 2018	2018	NA	ACI	birds	T	sound_richness	acoustic	NO	7	-0.1125000	0.2500000	Journal of ecoacoustics
90	109	Ferreira et al. 2018	2018	NA	ACI	invertebrates	T	sound_richness	acoustic	NO	7	0.1765000	0.2500000	Journal of ecoacoustics
90	110	Ferreira et al. 2018	2018	NA	ACI	mammals	T	sound_richness	acoustic	NO	7	-0.0598000	0.2500000	Journal of ecoacoustics
90	111	Ferreira et al. 2018	2018	NA	ADI	anurans	T	sound_richness	acoustic	NO	7	1.0277000	0.2500000	Journal of ecoacoustics
90	115	Ferreira et al. 2018	2018	NA	ADI	birds	T	sound_richness	acoustic	NO	7	-0.4747000	0.2500000	Journal of ecoacoustics
90	116	Ferreira et al. 2018	2018	NA	ADI	invertebrates	T	sound_richness	acoustic	NO	7	0.8162000	0.2500000	Journal of ecoacoustics
90	117	Ferreira et al. 2018	2018	NA	ADI	mammals	T	sound_richness	acoustic	NO	7	0.3826000	0.2500000	Journal of ecoacoustics
90	118	Ferreira et al. 2018	2018	NA	AEI	anurans	T	sound_richness	acoustic	NO	7	-0.9660000	0.2500000	Journal of ecoacoustics
90	122	Ferreira et al. 2018	2018	NA	AEI	birds	T	sound_richness	acoustic	NO	7	0.6011000	0.2500000	Journal of ecoacoustics
90	123	Ferreira et al. 2018	2018	NA	AEI	invertebrates	T	sound_richness	acoustic	NO	7	-0.7058000	0.2500000	Journal of ecoacoustics
90	124	Ferreira et al. 2018	2018	NA	AEI	mammals	T	sound_richness	acoustic	NO	7	-0.4389000	0.2500000	Journal of ecoacoustics
90	125	Ferreira et al. 2018	2018	NA	BIO	anurans	T	sound_richness	acoustic	NO	7	-0.1743000	0.2500000	Journal of ecoacoustics
90	129	Ferreira et al. 2018	2018	NA	BIO	birds	T	sound_richness	acoustic	NO	7	-0.1358000	0.2500000	Journal of ecoacoustics
90	130	Ferreira et al. 2018	2018	NA	BIO	invertebrates	T	sound_richness	acoustic	NO	7	-0.0556000	0.2500000	Journal of ecoacoustics
90	131	Ferreira et al. 2018	2018	NA	BIO	mammals	T	sound_richness	acoustic	NO	7	-0.0839000	0.2500000	Journal of ecoacoustics
90	132	Ferreira et al. 2018	2018	NA	H	anurans	T	sound_richness	acoustic	NO	7	1.2090000	0.2500000	Journal of ecoacoustics
90	136	Ferreira et al. 2018	2018	NA	H	birds	T	sound_richness	acoustic	NO	7	-0.5048000	0.2500000	Journal of ecoacoustics
90	137	Ferreira et al. 2018	2018	NA	H	invertebrates	T	sound_richness	acoustic	NO	7	0.8949000	0.2500000	Journal of ecoacoustics
90	138	Ferreira et al. 2018	2018	NA	H	mammals	T	sound_richness	acoustic	NO	7	0.4169000	0.2500000	Journal of ecoacoustics
90	142	Ferreira et al. 2018	2018	NA	NDSI	anurans	T	sound_richness	acoustic	NO	7	1.1933000	0.2500000	Journal of ecoacoustics
90	146	Ferreira et al. 2018	2018	NA	NDSI	birds	T	sound_richness	acoustic	NO	7	-0.5567000	0.2500000	Journal of ecoacoustics
90	147	Ferreira et al. 2018	2018	NA	NDSI	invertebrates	T	sound_richness	acoustic	NO	7	0.8485000	0.2500000	Journal of ecoacoustics
90	148	Ferreira et al. 2018	2018	NA	NDSI	mammals	T	sound_richness	acoustic	NO	7	0.4513000	0.2500000	Journal of ecoacoustics
92	88	Torti et al. 2018	2018	1.950	ACI	mammals	T	abundance	no_acoustic	NO	258	0.6150000	0.0039000
92	89	Torti et al. 2018	2018	1.950	ADI	mammals	T	abundance	no_acoustic	NO	258	0.0174000	0.0039000
92	90	Torti et al. 2018	2018	1.950	AR	mammals	T	abundance	no_acoustic	NO	258	0.0266000	0.0039000
92	91	Torti et al. 2018	2018	1.950	H	mammals	T	abundance	no_acoustic	NO	258	0.1404000	0.0039000
96	105	Izaguirre et al.  2018	2018	NA	ACI	birds	T	abundance	no_acoustic	YES	12	0.7315000	0.1111000	Journal of ecoacoustics
96	106	Izaguirre et al.  2018	2018	NA	ACI	birds	T	diversity	no_acoustic	YES	12	-0.7068500	0.0833250	Journal of ecoacoustics
96	107	Izaguirre et al.  2018	2018	NA	ACI	birds	T	richness	no_acoustic	YES	12	-0.7941000	0.1111000	Journal of ecoacoustics
96	112	Izaguirre et al.  2018	2018	NA	ADI	birds	T	abundance	no_acoustic	YES	12	-0.7958000	0.1111000	Journal of ecoacoustics
96	113	Izaguirre et al.  2018	2018	NA	ADI	birds	T	diversity	no_acoustic	YES	12	0.6084000	0.0833250	Journal of ecoacoustics
96	114	Izaguirre et al.  2018	2018	NA	ADI	birds	T	richness	no_acoustic	YES	12	0.5192000	0.1111000	Journal of ecoacoustics
96	119	Izaguirre et al.  2018	2018	NA	AEI	birds	T	abundance	no_acoustic	YES	12	0.7430000	0.1111000	Journal of ecoacoustics
96	120	Izaguirre et al.  2018	2018	NA	AEI	birds	T	diversity	no_acoustic	YES	12	-0.5394000	0.0833250	Journal of ecoacoustics
96	121	Izaguirre et al.  2018	2018	NA	AEI	birds	T	richness	no_acoustic	YES	12	-0.4181000	0.1111000	Journal of ecoacoustics
96	126	Izaguirre et al.  2018	2018	NA	BIO	birds	T	abundance	no_acoustic	YES	12	0.5553000	0.1111000	Journal of ecoacoustics
96	127	Izaguirre et al.  2018	2018	NA	BIO	birds	T	diversity	no_acoustic	YES	12	-0.6801500	0.0833250	Journal of ecoacoustics
96	128	Izaguirre et al.  2018	2018	NA	BIO	birds	T	richness	no_acoustic	YES	12	-0.5485000	0.1111000	Journal of ecoacoustics
96	133	Izaguirre et al.  2018	2018	NA	H	birds	T	abundance	no_acoustic	YES	12	-0.3904000	0.1111000	Journal of ecoacoustics
96	134	Izaguirre et al.  2018	2018	NA	H	birds	T	diversity	no_acoustic	YES	12	0.3747000	0.0833250	Journal of ecoacoustics
96	135	Izaguirre et al.  2018	2018	NA	H	birds	T	richness	no_acoustic	YES	12	0.5773000	0.1111000	Journal of ecoacoustics
96	139	Izaguirre et al.  2018	2018	NA	M	birds	T	abundance	no_acoustic	YES	12	0.4944000	0.1111000	Journal of ecoacoustics
96	140	Izaguirre et al.  2018	2018	NA	M	birds	T	diversity	no_acoustic	YES	12	-0.6604000	0.0833250	Journal of ecoacoustics
96	141	Izaguirre et al.  2018	2018	NA	M	birds	T	richness	no_acoustic	YES	12	-0.6686000	0.1111000	Journal of ecoacoustics
96	143	Izaguirre et al.  2018	2018	NA	NDSI	birds	T	abundance	no_acoustic	YES	12	-0.0955000	0.1111000	Journal of ecoacoustics
96	144	Izaguirre et al.  2018	2018	NA	NDSI	birds	T	diversity	no_acoustic	YES	12	0.2976500	0.0833250	Journal of ecoacoustics
96	145	Izaguirre et al.  2018	2018	NA	NDSI	birds	T	richness	no_acoustic	YES	12	0.4012000	0.1111000	Journal of ecoacoustics
96	149	Izaguirre et al.  2018	2018	NA	NP	birds	T	abundance	no_acoustic	YES	12	-0.5983000	0.1111000	Journal of ecoacoustics
96	150	Izaguirre et al.  2018	2018	NA	NP	birds	T	diversity	no_acoustic	YES	12	0.6247500	0.0833250	Journal of ecoacoustics
96	151	Izaguirre et al.  2018	2018	NA	NP	birds	T	richness	no_acoustic	YES	12	0.6372000	0.1111000	Journal of ecoacoustics
251	168	Buxton et al. 2016	2016	2.440	ACI	birds	T	diversity	acoustic	NO	72	0.2300000	0.0108750	Ecol. Evol.
251	169	Buxton et al. 2016	2016	2.440	ACI	birds	T	richness	acoustic	NO	72	0.3673000	0.0145000	Ecol. Evol.
427	27	Fuller et al. 2015	2015	3.190	ACI	birds	T	richness	acoustic	NO	380	0.0503000	0.0027000	Ecol. Indic.
427	28	Fuller et al. 2015	2015	3.190	ADI	birds	T	richness	acoustic	NO	380	0.0395000	0.0027000	Ecol. Indic.
427	29	Fuller et al. 2015	2015	3.190	AEI	birds	T	richness	acoustic	NO	380	0.0864000	0.0027000	Ecol. Indic.
427	30	Fuller et al. 2015	2015	3.190	BIO	birds	T	richness	acoustic	NO	380	0.0543000	0.0027000	Ecol. Indic.
427	31	Fuller et al. 2015	2015	3.190	H	birds	T	richness	acoustic	NO	380	0.1281000	0.0027000	Ecol. Indic.
427	32	Fuller et al. 2015	2015	3.190	NDSI	birds	T	richness	acoustic	NO	380	0.1517000	0.0027000	Ecol. Indic.
1132	10	Depraetere et al. 2012	2012	2.890	AR	birds	T	richness	acoustic	NO	12	1.7295000	0.1111000	Ecol. Indic.
1177	5	Joo et al. 2011	2011	2.170	H	birds	T	richness	acoustic	YES	5	0.1820000	0.5000000	Landsc. Urban Plan.
1262	6	Pieretti et al. 2011	2011	2.700	ACI	birds	T	sound_abundance	acoustic	YES	20	1.2568600	0.0352800	Ecol. Indic.
2740	69	Mammides et al. 2017	2017	3.980	ACI	birds	T	diversity	no_acoustic	NO	47	0.1614000	0.0227000	Ecol. Indic.
2740	70	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	47	0.2132000	0.0227000	Ecol. Indic.
2740	72	Mammides et al. 2017	2017	3.980	ADI	birds	T	diversity	no_acoustic	NO	47	0.3769000	0.0227000	Ecol. Indic.
2740	73	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	47	0.3654000	0.0227000	Ecol. Indic.
2740	74	Mammides et al. 2017	2017	3.980	AEI	birds	T	diversity	no_acoustic	NO	47	-0.4118000	0.0227000	Ecol. Indic.
2740	75	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	47	-0.4236000	0.0227000	Ecol. Indic.
2740	76	Mammides et al. 2017	2017	3.980	AR	birds	T	diversity	no_acoustic	NO	47	-0.4847000	0.0227000	Ecol. Indic.
2740	77	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	47	-0.4973000	0.0227000	Ecol. Indic.
2740	78	Mammides et al. 2017	2017	3.980	BIO	birds	T	diversity	no_acoustic	NO	47	-0.2986000	0.0227000	Ecol. Indic.
2740	79	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	47	-0.3541000	0.0227000	Ecol. Indic.
2740	81	Mammides et al. 2017	2017	3.980	H	birds	T	diversity	no_acoustic	NO	47	0.5361000	0.0227000	Ecol. Indic.
2740	82	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	47	0.5627000	0.0227000	Ecol. Indic.
2740	83	Mammides et al. 2017	2017	3.980	NDSI	birds	T	diversity	no_acoustic	NO	47	-0.0100000	0.0227000	Ecol. Indic.
2740	84	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	47	-0.0100000	0.0227000	Ecol. Indic.
2740	182	Mammides et al. 2017	2017	3.980	ACI	birds	T	diversity	no_acoustic	NO	47	0.0601000	0.0227000	Ecol. Indic.
2740	183	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	47	-0.0300000	0.0227000	Ecol. Indic.
2740	184	Mammides et al. 2017	2017	3.980	ADI	birds	T	diversity	no_acoustic	NO	47	0.7250000	0.0227000	Ecol. Indic.
2740	185	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	47	0.6184000	0.0227000	Ecol. Indic.
2740	187	Mammides et al. 2017	2017	3.980	AEI	birds	T	diversity	no_acoustic	NO	47	-0.7089000	0.0227000	Ecol. Indic.
2740	188	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	47	-0.6042000	0.0227000	Ecol. Indic.
2740	189	Mammides et al. 2017	2017	3.980	AR	birds	T	diversity	no_acoustic	NO	47	-0.2448000	0.0227000	Ecol. Indic.
2740	190	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	47	-0.2448000	0.0227000	Ecol. Indic.
2740	191	Mammides et al. 2017	2017	3.980	BIO	birds	T	diversity	no_acoustic	NO	47	0.2027000	0.0227000	Ecol. Indic.
2740	192	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	47	0.2342000	0.0227000	Ecol. Indic.
2740	193	Mammides et al. 2017	2017	3.980	H	birds	T	diversity	no_acoustic	NO	47	0.7928000	0.0227000	Ecol. Indic.
2740	194	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	47	0.6777000	0.0227000	Ecol. Indic.
2740	195	Mammides et al. 2017	2017	3.980	NDSI	birds	T	diversity	no_acoustic	NO	47	0.6931000	0.0227000	Ecol. Indic.
2740	196	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	47	0.6042000	0.0227000	Ecol. Indic.
2740	240	Mammides et al. 2017	2017	3.980	ACI	birds	T	diversity	no_acoustic	NO	50	0.0601000	0.0213000	Ecol. Indic.
2740	241	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	50	0.0400000	0.0213000	Ecol. Indic.
2740	242	Mammides et al. 2017	2017	3.980	ADI	birds	T	diversity	no_acoustic	NO	50	0.5101000	0.0213000	Ecol. Indic.
2740	243	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	50	0.6328000	0.0213000	Ecol. Indic.
2740	244	Mammides et al. 2017	2017	3.980	AEI	birds	T	diversity	no_acoustic	NO	50	-0.4973000	0.0213000	Ecol. Indic.
2740	245	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	50	-0.6475000	0.0213000	Ecol. Indic.
2740	246	Mammides et al. 2017	2017	3.980	AR	birds	T	diversity	no_acoustic	NO	50	-0.1003000	0.0213000	Ecol. Indic.
2740	247	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	50	-0.0802000	0.0213000	Ecol. Indic.
2740	248	Mammides et al. 2017	2017	3.980	BIO	birds	T	diversity	no_acoustic	NO	50	0.2237000	0.0213000	Ecol. Indic.
2740	249	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	50	0.3884000	0.0213000	Ecol. Indic.
2740	250	Mammides et al. 2017	2017	3.980	H	birds	T	diversity	no_acoustic	NO	50	0.3095000	0.0213000	Ecol. Indic.
2740	251	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	50	0.3769000	0.0213000	Ecol. Indic.
2740	252	Mammides et al. 2017	2017	3.980	NDSI	birds	T	diversity	no_acoustic	NO	50	0.2769000	0.0213000	Ecol. Indic.
2740	253	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	50	0.3654000	0.0213000	Ecol. Indic.
2740	287	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.0701000	0.1429000	Ecol. Indic.
2740	288	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6042000	0.1429000	Ecol. Indic.
2740	289	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6625000	0.1429000	Ecol. Indic.
2740	290	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.1003000	0.1429000	Ecol. Indic.
2740	291	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.1923000	0.1429000	Ecol. Indic.
2740	292	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4236000	0.1429000	Ecol. Indic.
2740	293	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.3769000	0.1429000	Ecol. Indic.
2740	302	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.0300000	0.1429000	Ecol. Indic.
2740	303	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6475000	0.1429000	Ecol. Indic.
2740	304	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6777000	0.1429000	Ecol. Indic.
2740	305	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.1003000	0.1429000	Ecol. Indic.
2740	306	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.2237000	0.1429000	Ecol. Indic.
2740	307	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4722000	0.1429000	Ecol. Indic.
2740	308	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.4847000	0.1429000	Ecol. Indic.
2740	313	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.0701000	0.1429000	Ecol. Indic.
2740	314	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6184000	0.1429000	Ecol. Indic.
2740	315	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6777000	0.1429000	Ecol. Indic.
2740	316	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.0601000	0.1429000	Ecol. Indic.
2740	317	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.2237000	0.1429000	Ecol. Indic.
2740	318	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4118000	0.1429000	Ecol. Indic.
2740	319	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.3316000	0.1429000	Ecol. Indic.
2740	328	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.0300000	0.1429000	Ecol. Indic.
2740	329	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6184000	0.1429000	Ecol. Indic.
2740	330	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6777000	0.1429000	Ecol. Indic.
2740	331	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.0601000	0.1429000	Ecol. Indic.
2740	332	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.2342000	0.1429000	Ecol. Indic.
2740	333	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4236000	0.1429000	Ecol. Indic.
2740	334	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.3884000	0.1429000	Ecol. Indic.
2740	341	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.1104000	0.1429000	Ecol. Indic.
2740	342	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6042000	0.1429000	Ecol. Indic.
2740	343	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6475000	0.1429000	Ecol. Indic.
2740	344	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.0701000	0.1429000	Ecol. Indic.
2740	345	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.2342000	0.1429000	Ecol. Indic.
2740	346	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4236000	0.1429000	Ecol. Indic.
2740	347	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.4477000	0.1429000	Ecol. Indic.
2740	352	Mammides et al. 2017	2017	3.980	ACI	birds	T	richness	no_acoustic	NO	10	0.0902000	0.1429000	Ecol. Indic.
2740	353	Mammides et al. 2017	2017	3.980	ADI	birds	T	richness	no_acoustic	NO	10	0.6328000	0.1429000	Ecol. Indic.
2740	354	Mammides et al. 2017	2017	3.980	AEI	birds	T	richness	no_acoustic	NO	10	-0.6777000	0.1429000	Ecol. Indic.
2740	355	Mammides et al. 2017	2017	3.980	AR	birds	T	richness	no_acoustic	NO	10	-0.1003000	0.1429000	Ecol. Indic.
2740	356	Mammides et al. 2017	2017	3.980	BIO	birds	T	richness	no_acoustic	NO	10	0.2027000	0.1429000	Ecol. Indic.
2740	357	Mammides et al. 2017	2017	3.980	H	birds	T	richness	no_acoustic	NO	10	0.4599000	0.1429000	Ecol. Indic.
2740	358	Mammides et al. 2017	2017	3.980	NDSI	birds	T	richness	no_acoustic	NO	10	0.4356000	0.1429000	Ecol. Indic.
2745	2	Sueur et al. 2008	2008	4.810	H	several	T	richness	acoustic	NO	10	1.7211000	0.1429000	PLoS One
2977	97	Jorge et al. 2018	2018	4.490	ACI	birds	T	richness	acoustic	YES	9	0.5731000	0.1667000	Ecol. Indic.
2977	99	Jorge et al. 2018	2018	4.490	ADI	birds	T	richness	acoustic	YES	9	-0.2672000	0.1667000	Ecol. Indic.
2977	100	Jorge et al. 2018	2018	4.490	AEI	birds	T	richness	acoustic	YES	9	0.3123000	0.1667000	Ecol. Indic.
2977	101	Jorge et al. 2018	2018	4.490	BIO	birds	T	richness	acoustic	YES	9	0.4181000	0.1667000	Ecol. Indic.
2977	102	Jorge et al. 2018	2018	4.490	H	birds	T	richness	acoustic	YES	9	-0.1475000	0.1667000	Ecol. Indic.
2977	103	Jorge et al. 2018	2018	4.490	NDSI	birds	T	richness	acoustic	YES	9	0.3352000	0.1667000	Ecol. Indic.
2977	205	Jorge et al. 2018	2018	4.490	ACI	birds	T	richness	acoustic	YES	9	0.3123000	0.1667000	Ecol. Indic.
2977	207	Jorge et al. 2018	2018	4.490	ADI	birds	T	richness	acoustic	YES	9	-0.4676000	0.1667000	Ecol. Indic.
2977	208	Jorge et al. 2018	2018	4.490	AEI	birds	T	richness	acoustic	YES	9	0.5192000	0.1667000	Ecol. Indic.
2977	209	Jorge et al. 2018	2018	4.490	BIO	birds	T	richness	acoustic	YES	9	0.3009000	0.1667000	Ecol. Indic.
2977	210	Jorge et al. 2018	2018	4.490	H	birds	T	richness	acoustic	YES	9	-0.1582000	0.1667000	Ecol. Indic.
2977	211	Jorge et al. 2018	2018	4.490	NDSI	birds	T	richness	acoustic	YES	9	0.3352000	0.1667000	Ecol. Indic.
2986	68	Raynor et al. 2017	2017	2.720	ACI	birds	T	richness	acoustic	YES	6	0.2448000	0.3333000	Condor

Table S2 - Variable descriptions for Table S2.

vd <- data.frame(Variables = c("id", "entry", "journal", "year", "impact_factor", "index",
                               "taxa", "environ", "bio", "diversity_source", "pseudoreplication",
                               "n", "z", "var"),
                 Descriptions = c(
                      "Identification number for the study",
                      "Identification number for the effect size (row entry)",
                      "Journal where the study was published",
                      "Year of publication",  
                      "Impact factor of the Journal",
                      "Acoustic index used",
                      "Primary studied group (invertebrates, fish, anurans, birds, mammals or several)",
                      "Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial)",
                      "Diversity metric used to correlate with acoustic index values",  
                      paste("Source of biological data. ",
                            "Sound recordings (coded as acoustic) or other source, ",
                            "i.e. literature, field surveys, etc. (coded as no_acoustic)."),
                      paste("Pseudoreplicated design to compute acoustic indices",
                            "relation with biodiversity (coded as YES). Coded as NO otherwise."),  
                      "Sample size adjusted for what was considered the number of true replicates",
                      "Fisher's Z effect size",
                      "Fisher's Z variance"
             ) 
           )
pander(vd, justify = "left")

Variables	Descriptions
id	Identification number for the study
entry	Identification number for the effect size (row entry)
journal	Journal where the study was published
year	Year of publication
impact_factor	Impact factor of the Journal
index	Acoustic index used
taxa	Primary studied group (invertebrates, fish, anurans, birds, mammals or several)
environ	Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial)
bio	Diversity metric used to correlate with acoustic index values
diversity_source	Source of biological data. Sound recordings (coded as acoustic) or other source, i.e. literature, field surveys, etc. (coded as no_acoustic).
pseudoreplication	Pseudoreplicated design to compute acoustic indices relation with biodiversity (coded as YES). Coded as NO otherwise.
n	Sample size adjusted for what was considered the number of true replicates
z	Fisher’s Z effect size
var	Fisher’s Z variance

Dataset overview

In what follows, we give a brief overview of the dataset mostly by way of tables and figures.

Our dataset comprised a total of 34 studies and 364 effect sizes. Therefore, most studies contributed with more than one effect size for the meta-analysis.

Table S3 - Number of effect sizes collected from each of the 34 studies included in the meta-analysis. ID corresponds to the study identification number in our dataset.

studies_n <- as.data.frame(table(df_tidy$id), stringsAsFactors = FALSE)
studies_n <- cbind(unique(df_tidy$authors), studies_n)
colnames(studies_n) <- c("Study", "ID", "Effect_sizes")
studies_n <- studies_n %>%
              select(ID, Study, Effect_sizes) %>%
              arrange(-Effect_sizes)

kable(studies_n, format = "html") %>%
  kable_styling(c("striped"))

ID	Study	Effect_sizes
2740	Mammides et al. 2017	84
53	Moreno-Gomez 2019	42
87	Eldridge et al. 2018	28
80	Staaterman et al. 2017	24
90	Ferreira et al. 2018	24
96	Izaguirre et al.  2018	24
10	Buscaino et al. 2016	22
2	Desjonquères et al. 2015	12
11	Bertucci et al. 2016	12
89	Gage et al. 2017	12
2977	Jorge et al. 2018	12
70	Bolgan et al. 2018	11
9	Harris et al. 2016	6
86	Indraswari et al. 2018	6
427	Fuller et al. 2015	6
14	Wa Maina et al. 2016	4
60	Patrick Lyon et al. 2019	4
77	Fairbrass et al. 2017	4
92	Torti et al. 2018	4
15	Roca & Proulx 2016	3
45	McLaren 2012	3
13	McWilliam & Hawkin 2013	2
41	Paisley-Jones 2011	2
44	Machado et al. 2017	2
251	Buxton et al. 2016	2
4	Parks et al. 2014	1
6	Boelman et al. 2007	1
17	Zhang et al. 2015	1
37	Picciulin et al. 2016	1
1132	Depraetere et al. 2012	1
1177	Joo et al. 2011	1
1262	Pieretti et al. 2011	1
2745	Sueur et al. 2008	1
2986	Raynor et al. 2017	1

The most studied acoustic index was ACI and the most used biodiversity metric was ‘species richness’.

Table S4 - Number of effect sizes and studies per moderator levels.

# Table for moderator levels
mods <- c("index", "bio", "diversity_source", "environ")
sample_sizes <- do.call(rbind, lapply(mods, function(x) n_studies_entries(df_tidy, x)))
# Format output
sample_sizes <- cbind(row.names(sample_sizes), sample_sizes)
sample_sizes <- as.data.frame(sample_sizes)
names(sample_sizes) <- c("Moderator_levels", "Effect_sizes", "Studies")
sample_sizes$Moderator_levels <- str_replace(sample_sizes$Moderator_levels, "^([a-z])", toupper)
n_row <- nrow(sample_sizes)
sample_sizes$Moderator_levels[(n_row - 1):n_row] <- c("Aquatic", "Terrestrial")

kable(sample_sizes, format = "html", row.names = FALSE) %>%
    kable_styling("striped", full_width = FALSE, position = "left") %>%
    row_spec(0, font_size = 16, bold = TRUE) %>%
    pack_rows("Acoustic indices", 1, 11) %>%
    pack_rows("Biodiversity metrics", 12, 16) %>%
    pack_rows("Diversity source", 17, 18) %>%
    pack_rows("Environment", 19, 20)

Moderator_levels	Effect_sizes	Studies
Acoustic indices
ACI	113	25
ADI	38	12
AEI	34	8
AR	18	5
BIO	36	10
H	55	16
Hf	12	3
Ht	15	4
M	5	2
NDSI	33	10
NP	5	2
Biodiversity metrics
Abundance	27	6
Diversity	49	9
Richness	187	21
Sound_abundance	66	11
Sound_richness	35	3
Diversity source
Acoustic	200	26
No_acoustic	164	11
Environment
Aquatic	95	10
Terrestrial	269	24

Visual description of eligible studies

We gathered studies from 6 of the 7 continents (no studies in Antarctica). Most studies were conducted in USA, France, Australia and Brazil.

knitr::include_graphics("rmd/mapa.png")

Figure S2 - The geographic distribution of the study sites corresponding to the 35 studies used in meta-analysis. The coloring of countries exhibits a white to black gradient relative to an increase in the number of studies contributed by each country. The colored dots discriminate between different groups of studied taxon.

The performance of acoustic indices as a biodiversity indicators was mainly assessed with species richness and sound abundance as biodiversity metrics.

knitr::include_graphics("rmd/heatmap.png")

Figure S3 - Distribution of the 35 articles by biodiversity parameters, taxa and acoustic indices studied. The graph shows the number of articles for each biodiversity parameter and taxonomic group for the each acoustic indices.

A large set of studies (40%) exhibited statistical deficiencies in testing the relationship between acoustic indices and biodiversity estimates due to pseudoreplication.

knitr::include_graphics("rmd/ps.index.png")

Figure S4 - Pseudoreplication summary. The data is representing the total number of articles for each index. Color orange represents number of pseudo-replicated studies and green non pseudo-replicated studies. The article (Papin et al., 2019b) was withdrawal from the pseudoreplication analysis due to impossibility of obtain the pseudoreplication data causing some variation on the total number of each acoustic index..

Meta-analysis

We clustered effect sizes within their corresponding studies and conducted multilevel meta-analysis using Fisher’s Z as our response variable. The multilevel structure accounted for the correlation structures within studies and thus allowed the inclusion of multiple effect sizes per study.

Summary effect size

We tested whether acoustic indices were good estimators of biodiversity by computing an intercept-only model. The resulting summary effect size not only gives a clue of whether acoustic indices are performing well in estimating biodiversity across the literature, but also allows us to check if there is substantial heterogeneity in our effect sizes that could be explained by moderators.

Intercept-only meta-analysis output

res_main <- rma.mv(z, var, random = ~1 | id/entry, data = df_tidy)
res_main

## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0458  0.2139     34     no        id 
## sigma^2.2  0.1755  0.4190    364     no  id/entry 
## 
## Test for Heterogeneity:
## Q(df = 363) = 2220.9097, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.3461  0.0577  6.0014  <.0001  0.2331  0.4591  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S5 - Resulting estimates from intercept-only model converted to Pearson’s correlation. “Estimate” is the Pearson’s r summary effect size. “CI.lb” and “CI.ub” are the confidence intervals lower and upper bounds, respectively.

r_main <- sapply(c(r = res_main$b, CI.lb = res_main$ci.lb, CI.ub = res_main$ci.ub), z2r)
r_main_pander <- r_main
names(r_main_pander) <- c("Estimate", names(r_main)[2:3])
pander(r_main_pander)

Estimate	CI.lb	CI.ub
0.3329	0.2289	0.4294

The summary estimate indicates that acoustic indices showed a moderate correlation with biodiversity metrics. However this result does not say nothing about differences in performance between acoustic indices or context-dependencies due to, for example, different environments or diversity metrics. For this we would need to inspect moderators, but before we need to check if our intercept-only model has unexplained variance that can be partitioned by our chosen moderators.

Effect sizes in ascending order

The amount of heterogeneity in effect sizes can be coarsely inspected by plotting all effect sizes and respective variances, and see their dispersion along the x-axis.

df_all_plt <- mutate(df_tidy, z = z2r(z), var = z2r(var))
r_main <- as.data.frame(t(r_main))
nudge <- 2
overall_y <- -nudge - 1
ggplot(data = df_all_plt, aes(x = z, y = reorder(entry, -z))) + 
    geom_errorbarh(aes(xmin = z - 1.96 * var, xmax = z + 1.96 * var),
                   height = 0, size = 0.5, color = "grey", 
                   position = position_nudge(y = nudge)) + 
    geom_point(size = 0.8, color = "darkgreen", 
               position = position_nudge(y = nudge)) +
    geom_segment(aes(x = z2r(res_main$b), y = overall_y, 
                     xend = z2r(res_main$b), 
                     yend = nrow(df_all_plt) + nudge + 10), 
                     color = alpha("forestgreen", 0.7), linetype = 2, size = 0.5) +   
    geom_vline(xintercept = 0, linetype = 1) +
    # Insert overall estimate
    geom_errorbarh(aes(xmin = r_main$CI.lb, xmax = r_main$CI.ub, y = overall_y), 
                   color = "grey") +
    geom_point(data = r_main, aes(x = r, y = overall_y), size = 3, color = "forestgreen") +
    geom_hline(yintercept = nudge - 1, color = alpha("black", 0.5), linetype = 5, size = 1) + 
    annotate("text", x = -1.4, y = overall_y, label= "Overall estimate", size = 4, adj = "right") + 
    scale_y_discrete(expand = c(0.025, 0.01)) +
    xlab("Effect size (r)") +
    ylab("Dataset entries ordered by effect size magnitude") +
    theme_minimal() + 
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_blank(),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title = element_text(size = 14),
          panel.grid.major.y = element_blank(), 
          legend.position = "none"
    )

Figure S5 - Pearson correlation effect sizes (r) in ascending order of magnitude from all dataset entries. Effect sizes higher than 0 (vertical line) represent a positive correlation between acoustic indices and diversity. Effect sizes below 0 indicate a negative correlation between acoustic indices and diversity. The green circles above the dashed horizontal line, are effect sizes means and corresponding 95% confidence intervals (grey horizontal lines). Below the dashed line is the summary effect size (green circle) and corresponding 95% confidence interval (grey horizontal lines) obtained after running the intercept-only model.

Check heterogeneity

We quantified heterogeneity with the I² statistic, which estimates the proportion of unknown variation in effect sizes not attributed to sampling error variance.

Table S6 - Unnacounted heterogeneity of the intercept-only model as measured by I² statistic. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).

font_css <- "font-family: Arial"
Is <- multilevel_I(res_main) * 100
Is_df <- data.frame(Is[1], Is[2])
names(Is_df) <- c("Within study", "Between study")
rownames(Is_df) <- c("% Unexplained variation")
total_I <- paste("Total heterogeneity: ", round(Is[1] + Is[2], 2), "%")
header <- c(3)
names(header) <- c(total_I)
kable(Is_df, format = "html", digits = 2) %>%
  kable_styling(c("striped", "bordered"), full_width = FALSE, 
                position = "center") %>%
  add_header_above(header, font_size = 16, bold = TRUE,
                   extra_css = font_css) %>%
  row_spec(0, font_size = 14, extra_css = font_css) %>%
  row_spec(1, font_size = 12, align = "center")

Total heterogeneity: 85.13 %
	Within study	Between study
% Unexplained variation	17.61	67.52

mlm.variance.distribution(res_main)

Figure S6 - Visual representation of how variance was distributed over the multilevel structure of the intercept-only model. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).

The value of I² = 85% corresponding to the amount of heterogeneity that remains unnacounted for in the intercept-only model, gives a green signal to proceed with the use of moderators that can potentially explain some of this variation.

Analysis of moderators

We extended the previous intercept-only model with the inclusion of moderators as fixed factors. For these analysis, all moderator levels with less than 5 studies were excluded as low study sizes are more liable to produce biased estimates. This led to the removal of the acoustic indices ‘Hf’, ‘Ht’, ‘M’ and ‘NP’, and the biodiversity parameter ‘sound richness’ from model fitting procedures.

Sub-group analysis

We conducted sub-group analysis with acoustic index as a moderator to assess which acoustic indices best correlate with biodiversity.

To specifically test whether the effect size estimates from each acoustic index were different from zero we removed the model intercept.

df_indices <-  clear_moderators(df_tidy, "index")

## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP

Meta-analysis output with acoustic indices as moderator and no intercept.

res_indices <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ index - 1, data = df_indices)
res_indices

## 
## Multivariate Meta-Analysis Model (k = 327; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0295  0.1716     34     no        id 
## sigma^2.2  0.1710  0.4135    327     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 320) = 1876.0325, p-val < .0001
## 
## Test of Moderators (coefficients 1:7):
## QM(df = 7) = 70.5454, p-val < .0001
## 
## Model Results:
## 
##            estimate      se    zval    pval    ci.lb   ci.ub 
## indexACI     0.3809  0.0685  5.5596  <.0001   0.2466  0.5152  *** 
## indexADI     0.2493  0.0977  2.5506  0.0108   0.0577  0.4408    * 
## indexAEI     0.0396  0.1048  0.3774  0.7059  -0.1658  0.2449      
## indexAR      0.0780  0.1354  0.5756  0.5649  -0.1875  0.3434      
## indexBIO     0.1950  0.1012  1.9266  0.0540  -0.0034  0.3934    . 
## indexH       0.5511  0.0903  6.1036  <.0001   0.3742  0.7281  *** 
## indexNDSI    0.4557  0.1037  4.3944  <.0001   0.2524  0.6589  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S7 - Resulting estimates from sub-group analysis. The ‘Estimate’ column is the Pearson correlation effect size; SE is the standard error and CI.lb and CI.up, the lower and upper bounds of the confidence intervals, respectively.

df_pred_indices <- get_predictions(res_indices, intercept = FALSE)
df_pred_indices$coef <- str_remove(df_pred_indices$coef, "index")
names(df_pred_indices) <- c("Index", "Estimate", "SE", "CI.lb", "CI.ub")
pander(df_pred_indices)

Index	Estimate	SE	CI.lb	CI.ub
ACI	0.363	0.068	0.242	0.474
ADI	0.244	0.097	0.058	0.414
AEI	0.04	0.104	-0.164	0.24
AR	0.078	0.135	-0.185	0.331
BIO	0.193	0.101	-0.003	0.374
H	0.501	0.09	0.358	0.622
NDSI	0.427	0.103	0.247	0.578

nentries_index <- rowSums(table(df_indices$index, df_indices$id))
nstudies_index <- rowSums(ifelse(table(df_indices$index, df_indices$id) > 0, 1, 0))
n_index <- paste0(nentries_index, " (", nstudies_index, ")")

df_indices_plt <- data.frame("index" = names(nstudies_index),
                             "es" = z2r(res_indices$beta), 
                             "se" = z2r(res_indices$se), 
                             "ci.lb" = z2r(res_indices$ci.lb),
                             "ci.ub" = z2r(res_indices$ci.ub),
                             "n" = nstudies_index)

ggplot(data = df_indices_plt, aes(x = es, y = index)) + 
    geom_point(aes(color = index), size = 4) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub, color= index),
                   height = 0) + 
    geom_vline(xintercept = 0, linetype = 1) +
    geom_vline(xintercept = z2r(res_main$b), color = "forestgreen", 
               linetype = 2) + 
    scale_y_discrete(limits = rev(df_indices_plt$index)) +
    scale_color_brewer(palette="Dark2") +
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.x = element_text(size = 14),
          axis.title.y = element_blank(),
          legend.position = "none"
    )

Figure S7 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from sub-group meta-analysis with acoustic indices as the grouping factor. Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. The dashed green vertical line represents the summary effect size obtained from the intercept only meta-analysis.

Meta-regression

We used meta-regression to check the effect of multiple moderators on the ability of acoustic indices to estimate biodiversity. We focused on four moderators that could alter the performance of biodiversity estimation, namely:

1. acoustic index - which acoustic index was used to estimate biodiversity;
2. biodiversity metrics – which metric of diversity was used to check the performance of acoustic index;
3. environment – if recordings were done in aquatic or terrestrial environments;
4. diversity source – if metric of diversity was obtained from examination of sound recordings of any other source (e.g. field sampling).

We set as intercept the following combination of moderator levels: ACI index, species richness, terrestrial environment and non-acoustic data source.

Due to low study sample size between most factor level combinations, we were constrained to use only an additive effects model.

mods <- c("index", "bio", "environ", "diversity_source")
df_full <- clear_moderators(df_tidy, mods)

## Levels dropped from dataframe:
##  Moderator index
##       Hf Ht M NP
##  Moderator bio
##       sound_richness
##  Moderator environ
##       
##  Moderator diversity_source
##      

Meta-analysis with acoustic indices, biodiversity metrics, environment and diversity source as moderators.

res_full <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_full)
 
res_full

## 
## Multivariate Meta-Analysis Model (k = 296; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0355  0.1884     33     no        id 
## sigma^2.2  0.1738  0.4168    296     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 284) = 1730.2577, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 27.4277, p-val = 0.0040
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                     0.3590  0.1416   2.5359  0.0112   0.0815   0.6365 
## indexADI                   -0.1294  0.1171  -1.1049  0.2692  -0.3590   0.1001 
## indexAEI                   -0.2916  0.1231  -2.3679  0.0179  -0.5329  -0.0502 
## indexAR                    -0.2735  0.1482  -1.8461  0.0649  -0.5639   0.0169 
## indexBIO                   -0.1449  0.1203  -1.2038  0.2287  -0.3807   0.0910 
## indexH                      0.1977  0.1092   1.8111  0.0701  -0.0162   0.4117 
## indexNDSI                   0.0840  0.1260   0.6667  0.5050  -0.1630   0.3310 
## bioabundance               -0.0815  0.1589  -0.5133  0.6078  -0.3929   0.2298 
## biodiversity               -0.0420  0.0950  -0.4423  0.6583  -0.2283   0.1442 
## biosound_abundance          0.2600  0.1470   1.7690  0.0769  -0.0281   0.5480 
## environA                   -0.0656  0.1484  -0.4422  0.6583  -0.3565   0.2252 
## diversity_sourceacoustic   -0.0091  0.1464  -0.0623  0.9504  -0.2962   0.2779 
##  
## intrcpt                   * 
## indexADI 
## indexAEI                  * 
## indexAR                   . 
## indexBIO 
## indexH                    . 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance        . 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Multicollinearity

Multicollinearity among our moderators was inspected with VIF and found not to be an issue in our model (VIF < 1.7 for all moderators – threshold value of 3 (Zuur, Ieno, & Elphick, 2010).

Table S8 - VIF values obtained for each moderator level.

vif_meta <- as.data.frame(vif.rma(res_full))
vif_meta <- tibble::rownames_to_column(vif_meta, "Moderators")
colnames(vif_meta)[2] <- "VIF" 
pander(vif_meta)

Moderators	VIF
indexADI	1.497
indexAEI	1.45
indexAR	1.25
indexBIO	1.478
indexH	1.472
indexNDSI	1.438
bioabundance	1.244
biodiversity	1.156
biosound_abundance	1.454
environA	1.393
diversity_sourceacoustic	1.623

Heterogeneity full model

Substantial heterogeneity remained to be explained after fitting the full model. So other factors not tested or an interaction among our moderators could have the ability to extract even more information from the dataset. For the latter, an increase in available studies on the correlation between acoustic indices and biodiversity is of the utmost important.

# Fit overall model with df_full (without levels with less than 5 studies)
res_main_full <- rma.mv(z, var, random = ~1 | id/entry, data = df_full)
sum_I_main_full <- sum(multilevel_I(res_main_full))
sum_I_full <- sum(multilevel_I(res_full))

cat("Heterogenity after fitting the full model\n\t", sum_I_full, "\n\n",
    "Difference heterogeneity between intercept only model and full model\n\t",
    sum_I_main_full - sum_I_full)

## Heterogenity after fitting the full model
##   0.8616913 
## 
##  Difference heterogeneity between intercept only model and full model
##   0.01210371

Full model results

Table S9 - Meta-regression results. The column “Coefficients” lists the model intercept and the levels of each moderator. The column “Estimate” is the estimated Pearson (r) correlation. “SE” is the standard error of the estimate. “CI” are the [lower] [upper] bounds of the confidence intervals.

df_pred_tbl <- get_predictions(res_full, format_table = TRUE, clean_labels = TRUE)
pander(df_pred_tbl)

Moderators	Coefficients	Estimate	SE	CI
Intercept	Intercept	0.344	0.141	[0.081] [0.563]
Index	ADI	0.226	0.147	[-0.061] [0.478]
Index	AEI	0.067	0.152	[-0.228] [0.351]
Index	AR	0.085	0.17	[-0.246] [0.399]
Index	BIO	0.211	0.149	[-0.08] [0.469]
Index	H	0.506	0.14	[0.273] [0.682]
Index	NDSI	0.416	0.148	[0.15] [0.626]
Bio	Abundance	0.271	0.164	[-0.047] [0.539]
Bio	Diversity	0.307	0.144	[0.033] [0.537]
Bio	Sound abundance	0.55	0.211	[0.197] [0.777]
Environment	Aquatic	0.285	0.142	[0.012] [0.519]
Source	Acoustic	0.336	0.108	[0.136] [0.51]

df_pred <- get_predictions(res_full)
colnames(df_pred) <- tolower(str_remove(colnames(df_pred), "\\s.*"))
df_pred$moderators <- df_pred_tbl$Moderators
df_pred$coef <- factor(df_pred_tbl$Coefficients, 
                       levels = rev(df_pred_tbl$Coefficients))

plt_colors <- c("#000000", brewer.pal(n = 4, name = "Dark2"))
ggplot(data = df_pred, aes(x = z2r(estimate), y = coef, color = moderators)) + 
    geom_point(size = 3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, size = 1) +
    geom_vline(xintercept = 0, linetype = 1) + 
    scale_color_manual(values = plt_colors, name = "Moderators") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 13, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 14),
          legend.text = element_text(size = 14)
    )

Figure S8 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from meta-regression analysis with moderators: acoustic indices (Index), biodiversity metrics (Bio), environment (Environment) and acoustic source (Source). Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero.

Test of moderators

We checked if our choice of moderators explained some of the variation in our effects sizes by computing a Wald-type test on the null hypothesis that moderator levels’ estimates are jointly equal to zero (Viechtbauer et al., 2015).

Table S10 - Wald-type tests for all moderators (first row), and for each moderator separately (remaining rows). “Q” is the Wald statistic. “df” are the degrees of freedom. “p” is the probability that moderator estimates came from a chi-square distribution, where all estimates are equal to zero. So a p-value < 0.05 gives support against the null hypothesis that moderator levels estimates are equal to zero (i.e. they do not explain variation in effect sizes).

imp_mods <- matrix(nrow = length(mods) + 1, ncol = 4, 
                   dimnames = list(NULL, c("Moderator", "Q", "df", "p")))
imp_mods <- as.data.frame(imp_mods)
# Add importance of all moderators
imp_mods[1, 2:ncol(imp_mods)] <- res_full[c("QM", "m", "QMp")]
imp_mods$Moderator[1] <- "All moderators"

for(i in 2:nrow(imp_mods)){
  wald_test <- anova(res_full, 
                     btt=grep(mods[i - 1], rownames(res_full$b)))[c("QM", "m","QMp")]
  imp_mods[i, 2:ncol(imp_mods)] <- wald_test
}
imp_mods$Moderator[2:nrow(imp_mods)] <- c("Acoustic indices", 
                                          "Biodiv. parameters", 
                                          "Environment", "Diversity source")
imp_mods <- mutate_if(imp_mods, is.numeric, round, 3)

pander(imp_mods)

Moderator	Q	df	p
All moderators	27.43	11	0.004
Acoustic indices	22.35	6	0.001
Biodiv. parameters	3.561	3	0.313
Environment	0.196	1	0.658
Diversity source	0.004	1	0.95

We found that the acoustic indices explained most of the variation in our full model. Hence, this suggests that acoustic indices are not equally performing when it comes to estimate biodiversity.

Difference between moderator levels

To assess pairwise-comparisons between moderator level estimates, we selected the moderator levels with the highest effect size estimates and compared these with effect size estimates for the other levels of the same moderator. For this, we again use a Wald-type test with one degree of freedom, on the null hypothesis that the difference between the two levels is equal to zero. Note that, if a moderator has only two levels, the comparison is directly retrieved from the model output.

Acoustic indices pairwise-comparisons

We compare the best indices, H and NDSI, with the other indices. We did not use ACI for comparisons, as comparisons can be obtained directly from the full model output.

H index

The pairwise-comparison for H index, suggest that the H index correlates better with biodiversity than the indices ADI, AEI, AR and BIO.

Table S11 - Wald-type tests for the constrasts between acoustic index H with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate H and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the H index and the estimate of the other index.

# Test differences between H and other indices
H_comp <- compare_moderators(df_full, res_full, "index", "H")
kable(H_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))

Compared	Estimate	SE	CI.lb	CI.up	QM	p
H - ADI	0.327	0.122	0.089	0.566	7.223	0.007
H - AEI	0.489	0.127	0.241	0.738	14.901	0.000
H - AR	0.471	0.152	0.173	0.769	9.623	0.002
H - BIO	0.343	0.124	0.099	0.586	7.591	0.006
H - ACI	0.198	0.109	-0.016	0.412	3.280	0.070
H - NDSI	0.114	0.130	-0.141	0.369	0.765	0.382

NDSI index

The pairwise-comparison for NDSI index, suggest that the NDSI index correlates better with biodiversity than the indices AEI and AR.

Table S12 - Wald-type tests for the constrasts between acoustic index NDSI with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate NDSI and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the NDSI index and the estimate of the other index.

# Test differences between NDSI and other indices
NDSI_comp <- compare_moderators(df_full, res_full, "index", "NDSI")
kable(NDSI_comp, format = "html", digits = 3) %>%
  kable_styling(c("strip", "condensed"))

Compared	Estimate	SE	CI.lb	CI.up	QM	p
NDSI - ADI	0.213	0.133	-0.047	0.474	2.586	0.108
NDSI - AEI	0.376	0.138	0.106	0.645	7.442	0.006
NDSI - AR	0.358	0.161	0.041	0.674	4.914	0.027
NDSI - BIO	0.229	0.135	-0.036	0.494	2.869	0.090
NDSI - H	-0.114	0.130	-0.369	0.141	0.765	0.382
NDSI - ACI	0.084	0.126	-0.163	0.331	0.444	0.505

Biodiversity metrics pairwise-comparisons

Since sound abundance measures seemed to be related with a best performance of the acoustic indices ability to correlate with biodiversity, we use sound abundance to compute pairwise comparisons with the other biodiversity metrics.

The pairwise-comparison for sound abundance gave marginal support (at p < 0.05) for the null hypothesis of no difference between sound abundance and other biodiversity metrics.

Table S13 - Wald-type tests for the contrasts between the biodiversity metric sound abundance with all other biodiversity metrics. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate sound abundance and the estimate of each of the other biodiversity metrics. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the sound abundance metric and the estimate of the other metric.

# Test difference between sound_abundance and other bio levels
sound_abund_comp <- compare_moderators(df_full, res_full, "bio", "sound_abundance")
kable(sound_abund_comp, format = "html", digits = 2) %>%
  kable_styling(c("strip", "condensed"))

Compared	Estimate	SE	CI.lb	CI.up	QM	p
sound_abundance - abundance	0.34	0.21	-0.08	0.76	2.53	0.11
sound_abundance - diversity	0.30	0.17	-0.03	0.64	3.09	0.08
sound_abundance - richness	0.26	0.15	-0.03	0.55	3.13	0.08

Publication bias

We assessed publication bias both, visually with funnel plots and statistically with Egger’s regression.

A funnel plot usually shows the relationship between effect sizes and standard errors. In a symmetric funnel plot, the dispersion of effect sizes should get narrower as standard error decreases.

Due to the multilevel structure of our dataset, we used meta-analytic residuals instead of effect sizes to reduce the effect of independence assumptions. We should consider publication bias as an issue if residuals are outside the expected symmetry of the funnel shape, and if some of the funnel sections do not contain any residual.

To statistically test for funnel plot symmetry we used Egger’s regression with no intercept. A non-significant inverse variance weighted regression of the residuals over the standard error, indicates that the deviation of the residuals from the funnel plot shape is not greater than what would be expected by chance in a symmetric funnel plots.

# Testing model residuals
resid <- rstandard(res_full)
eggers <- regtest(x = resid$resid, sei =sqrt(df_full$var), model = "lm")

funnel(res_full, 
       back = "white", 
       xlab = "Model Residuals", 
       ylab = "Std. Error", 
       pch = 21,
       col = "darkblue",
       cex = 1.1,
       lwd = 2
)
# Put eggers regression results on funnel plot
eggers_round <- round(eggers$pval, 2)
plt_text <- paste0("Regression test for plot symmetry \n\t\t  p = ", eggers_round, "\n\n")
legend(legend = plt_text, x = 0.5, y = -0.01, bg = alpha("darkgrey", 0.2))

Figure S9 - Funnel plot (dashed triangle) with the relation between model residuals from the meta-regression model, and effect size standard error. Absence of publication bias is represented by a scattered and symmetric distribution of values (blue hollow dots) within the funnel. The box on the top right is the p-value from Egger’s regression, which means that we failed to reject the null hypothesis of funnel symmetry (p = 0.53).

Output of Egger’s regression.

eggers$fit

## 
## Call:
## lm(formula = yi ~ X - 1, weights = 1/vi)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.9740  -1.0312  -0.2052   0.8569  17.3423 
## 
## Coefficients:
##      Estimate Std. Error t value Pr(>|t|)   
## X    -0.12346    0.04217  -2.927  0.00369 **
## Xsei  0.15381    0.23167   0.664  0.50727   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.466 on 294 degrees of freedom
## Multiple R-squared:  0.05028,    Adjusted R-squared:  0.04382 
## F-statistic: 7.782 on 2 and 294 DF,  p-value: 0.0005091

We could not find strong indications of publication bias. Notwithstanding the visual inspection of the funnel plot shows that there are some gaps in the dispersion of the dots in the funnel plot (special at the top, and at the bottom left corner).

Sensibility analysis

Pseudoreplication

Pseudoreplicated designs were widespread in our selected studies. Therefore, to determine the influence of effect size estimates from pseudoreplicated studies in our meta-analysis, we contrasted the effect size estimate for pseudoreplicated and non-pseudoreplicated studies. For this we conducted a meta-analysis with pseudoreplication as the single binary moderator and observed if the resulting estimate of the contrast between both designs overlapped zero.

Meta-analysis with pseudoreplication moderator

no_out <- capture.output({
            df_pseudorep <-  clear_moderators(df_tidy, "pseudoreplication")  
          })
  
res_pseudorep <- rma.mv(z, var, random = ~1 | id/entry, mods = ~ pseudoreplication, 
                        data = df_pseudorep)
res_pseudorep

## 
## Multivariate Meta-Analysis Model (k = 364; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.0508  0.2254     34     no        id 
## sigma^2.2  0.1754  0.4188    364     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 362) = 2176.1806, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4077, p-val = 0.5231
## 
## Model Results:
## 
##                       estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                 0.3759  0.0713   5.2759  <.0001   0.2363  0.5156  *** 
## pseudoreplicationYES   -0.0787  0.1232  -0.6385  0.5231  -0.3201  0.1628      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(data = df_pseudorep, aes(x = pseudoreplication, y = z2r(z))) +
  geom_boxplot(aes(color = pseudoreplication)) + 
  geom_jitter(aes(color = pseudoreplication, size = n), width = 0.2, alpha = 0.5) +
  geom_hline(yintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  ylab("Effect Size (r)") + 
  xlab("Pseudoreplicated") + 
  scale_color_brewer(palette = "Set1") + 
  scale_x_discrete(labels = c("No", "Yes")) +
  coord_flip() +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.y = element_text(size = 12, color = "black"),
        axis.text.x = element_text(size = 11, color = "black"),
        axis.title.y = element_text(size = 14),
        axis.title.x = element_text(size = 12),
        axis.line.x = element_line(color = "black")
  )

Figure S10 - Boxplot comparing effect size mean values of sampling designs considered pseudoreplicated (“Yes” on the vertical axis) against sampling designs not considered pseudoreplicated (“No” on the vertical axis). The circles represent each individual effect size mean value, and the circle size indicates the relative sample size of the effect size. The dashed green vertical line shows the position of summary effect size obtained from the intercept only meta-analysis.

We failed to find differences between the estimates of pseudoreplicated and non-pseudoreplicated designs. Thus, we believe that our adjustment of sample sizes, was a sufficient treatment for pseudoreplication in our meta-analysis.

Outliers

We visually inspected the presence of outlier studies using Cook’s distance clustered by studies. The Cook’s distance for a given study, refers to how far, on average, effect size estimates will move if the study in question is dropped from model fitting (Viechtbauer & Cheung, 2010). We considered a study an outlier if its Cook’s distance was higher than the average of all computed Cook’s distances.

Cook’s distance

### Cook's distances for each study!

cooks_dist <- cooks.distance(res_full, cluster=df_full$id)
df_full$id <- as.character(df_full$id)

df_cooks <- data.frame(id = names(cooks_dist), cooks_dist = cooks_dist)

df_cooks <- df_cooks %>% 
              left_join(df_full, by = "id") %>%
              select(id, authors, cooks_dist)
# Remove leading spaces
df_cooks$authors <- str_remove(df_cooks$authors, "^\\s")
df_cooks <- df_cooks %>%
              filter(!duplicated(authors)) %>%
              arrange(authors)

ggplot(data = df_cooks, aes(x = authors, y = cooks_dist, group = 1)) + 
  geom_point(color = "deepskyblue4") +
  geom_line(color = "deepskyblue4") + 
  geom_segment(aes(xend=authors), yend = 0, color = "darkgrey", linetype = 2) + 
  geom_hline(yintercept = mean(cooks_dist), color = "darkred", linetype = 2) + 
  xlab("Studies") + ylab("Cook's Distance") + 
  scale_x_discrete(limits = rev(df_cooks$authors)) + 
  theme_minimal() + 
  theme(
        axis.text = element_text(color = "black"),
        axis.line = element_line(color = "black"),
  ) + 
  coord_flip() 

Figure S11 - Cook’s distance values for each study (blue dots on the figure) and average Cook’s distance over all studies indicated as a dashed vertical red line. The Cook’s distance for a given study can be interpreted as the distance between the entire set of predicted values once with this study included and once with the this study excluded from the model fitting procedure. On the y-axis are the studies identified by first author and year. The x-axis corresponds to the Cook’s distance values.

Check outlier studies

Here, we examine outlier studies to discriminate possible reasons for their influence.

outliers <- df_cooks[which(df_cooks$cooks_dist > mean(df_cooks$cooks_dist)), ]$id

df_outliers <- df_full[which(df_full$id %in% outliers), ]

ggplot(data = df_outliers, aes(x = z2r(z), y = id)) + 
  geom_boxplot(aes(color = id), fill = NA, width = 0.3) + 
  geom_jitter(height = 0.1, aes(color = id)) + 
  scale_color_brewer(palette = "Set2") + 
  scale_y_discrete(labels = rev(unique(df_outliers$authors))) + 
  xlab("Effect size (r)") +
  geom_vline(xintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) + 
  theme_minimal() + 
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_text(size = 11, color = "black"),
    axis.text.x = element_text(size = 11, color = "black"),
    axis.line.x = element_line(color = "black"),
    legend.position = "none"
  )

Figure S12 - Boxplot and distribution of of effect size values (dots) of the two studies identified as outliers. The y-axis identifies the study, and the x-axis corresponds to the Pearson r effect size. The green vertical dashed line is the summary effect obtained in the intercept-only model.

Both outlier studies used birds as the organism of study, and assessed multiple acoustic indices (Gage et al. (2017) examined ACI, ADI, AEI, BIO, H, NDSI indices; and Mammides et al. (2017) examined ACI, ADI, AEI, AR, BIO, H, NDSI indices). The main difference was that Gage et al. (2017) used acoustic recordings to get biodiversity measures while Mammides et al. (2017) relied on non acoustic sources of biodiversity information.

The box plots and effect size dispersion, suggest that the study by Gage et al. (2017) contributed overdispersed effect sizes values, including some at the lower and high ends of the Pearson effect size scale of (-1 to 1).

The Mammides et al. (2017) study contributed a total 84 effect sizes, also dispersed over a wide range. The number of effect sizes per se (23% of total effect sizes) could be responsible for its high value of Cook’s distance.

Removing outlier studies and examining results

We evaluated the robustness of our results by removing the outliers from the dataset, and running the meta-regression model without the outlier studies.

Meta-analysis with outliers removed

df_no_outliers <- df_full[-which(df_full$id %in% outliers), ]

res_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, 
                   mods = ~ index + bio + environ + diversity_source, 
                   data = df_no_outliers)

res_no_outliers

## 
## Multivariate Meta-Analysis Model (k = 200; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed    factor 
## sigma^2.1  0.1225  0.3500     31     no        id 
## sigma^2.2  0.0557  0.2360    200     no  id/entry 
## 
## Test for Residual Heterogeneity:
## QE(df = 188) = 472.4368, p-val < .0001
## 
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 6.3074, p-val = 0.8521
## 
## Model Results:
## 
##                           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                     0.6457  0.1870   3.4522  0.0006   0.2791  1.0123 
## indexADI                   -0.2059  0.1118  -1.8412  0.0656  -0.4251  0.0133 
## indexAEI                   -0.0753  0.1260  -0.5977  0.5500  -0.3224  0.1717 
## indexAR                    -0.0770  0.2082  -0.3700  0.7114  -0.4850  0.3310 
## indexBIO                   -0.0992  0.1192  -0.8319  0.4054  -0.3328  0.1345 
## indexH                     -0.0357  0.1058  -0.3372  0.7360  -0.2430  0.1716 
## indexNDSI                  -0.0164  0.1406  -0.1165  0.9073  -0.2919  0.2591 
## bioabundance               -0.1566  0.1512  -1.0355  0.3005  -0.4530  0.1398 
## biodiversity               -0.1064  0.1298  -0.8199  0.4122  -0.3607  0.1479 
## biosound_abundance          0.1597  0.2105   0.7587  0.4480  -0.2528  0.5722 
## environA                   -0.1907  0.2057  -0.9271  0.3539  -0.5939  0.2125 
## diversity_sourceacoustic   -0.1379  0.1909  -0.7226  0.4700  -0.5120  0.2362 
##  
## intrcpt                   *** 
## indexADI                    . 
## indexAEI 
## indexAR 
## indexBIO 
## indexH 
## indexNDSI 
## bioabundance 
## biodiversity 
## biosound_abundance 
## environA 
## diversity_sourceacoustic 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# With outliers results from full model results 
df_pred$df <- "with_outliers"
# No outliers resuls in dataframe
df_no_outliers_plt <- get_predictions(res_no_outliers)
df_no_outliers_plt$moderators <- df_pred$moderators
df_no_outliers_plt$coef <- factor(df_pred$coef, 
                                  levels = rev(df_pred$coef))
df_no_outliers_plt$df <- "no_outliers"

df_examine_outliers <- rbind(df_pred, df_no_outliers_plt)

plt_colors <- c("skyblue4", "goldenrod3")#"seagreen")
pd <- position_dodge(0.6)
n_rows <- nrow(res_full$b)

ggplot(data = df_examine_outliers, aes(x = estimate, y = coef, color = df, position = df)) + 
    geom_point(position = pd, size = 2.3) +
    geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, position = pd, size = 0.8) + 
    geom_vline(xintercept = 0, linetype = 1, color = "black") + 
    #scale_y_discrete(labels =  rev(y_labels)) +
    scale_color_manual(values = plt_colors, name = "Dataset", 
                       labels = c("No outliers", "Full dataset")) + 
    geom_hline(yintercept= seq(1, n_rows - 1) + 0.5, linetype = 3, color = "black") + 
    theme_minimal() + 
    xlab("Effect size (r)") +
    theme(axis.text.x = element_text(size = 12, color = "black"),
          axis.text.y = element_text(size = 13, color = "black"),
          axis.line.x = element_line(color = "black"),
          axis.line.y = element_line(color = "black"),
          axis.title.y = element_blank(),
          axis.title.x = element_text(size = 14),
          panel.grid.major.y = element_blank(),
          legend.title = element_text(hjust = 0.5, size = 12),
          legend.text = element_text(size = 11)
    )

Figure S13 - Contrast of model estimates obtained with meta-regression analysis over the full dataset (yellow) and over the dataset with outliers removed (blue). Mean estimates are represented with circles and corresponding 95% confidence intervals with horizontal lines. We considered outliers every study that had a Cook’s distance value higher than the mean of all Cook distances. Model moderators were acoustic indices (ADI, AEI, AR, BIO, H, NDSI, with ACI as intercept), diversity metric (Abundance, Diversity, Sound abundance, with Richness as intercept), environment (Aquatic, with Terrestrial as intercept), diversity source (Acoustic, with Non-Acoustic as intercept). The solid vertical black line represents a null effect size.

The results are similar with major overlap between confidence intervals, specially for the most conclusive results in the full dataset. It seems that removing outliers had a tendency to generate stronger mean effect size estimates of the correlation between acoustic indices and biodiversity.

Heterogeneity for the intercept-only model with no outliers

res_main_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, data = df_no_outliers)

Is_no_outliers <- multilevel_I(res_main_no_outliers)

sum(Is_no_outliers)

## [1] 0.8256989

Effect size tendencies

We visually inspected tendencies in the effect size over year of publication, and impact factor of the journal.

Publication year

This figure indicates a decrease in effect size values over the years. ?SOME DISCUSSION HERE FOR REASONS? Roughly after the year of 2015, we see a surge in published studies so in the next figure we make a close up of published studies after 2015.

plt_color <- "darkorchid4"
ggplot(df_tidy, aes(x = year, y = z2r(z))) + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Publication Year") +  
    scale_x_continuous(breaks = seq(min(df_tidy$year), 
                                    max(df_tidy$year), by = 2)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S14 - Relation between effect size mean values (circles) and publication year. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.

Publication year > 2015

Fitting a regression line over studies from 2015 to 2019, the decreasing tendency persists but it is less pronounced.

df_tidy_after2015 <- df_tidy[df_tidy$year >= 2015, ]

ggplot(df_tidy_after2015, aes(x = year, y = z2r(z))) + 
    geom_jitter(aes(size = n), shape = 21, 
                fill = alpha(plt_color, 0.5), 
                color = plt_color) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Publication Year") +  
    scale_x_continuous(limits = c(2015, 2019),
                       breaks = seq(min(df_tidy_after2015$year), 
                                    max(df_tidy_after2015$year), 
                                    by = 1)) +
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S15 - Relation between effect size mean values (circles) and publication year after 2015 (inclusive) where there is a sudden and prominent rise of publications. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.

Impact factor

Effect size values do not appear to exhibit a tendency when it comes to journal impact factor.

plt_color <- "deeppink4"
ggplot(df_tidy, aes(x = impact_factor, y = z2r(z))) + 
  geom_jitter(aes(size = n), shape = 21, 
              fill = alpha(plt_color, 0.5), 
              color = plt_color,
              width = 0.2) + 
    geom_hline(yintercept=0, linetype = 2) +
    geom_smooth(method='lm', color = plt_color) +
    labs(y = "Effect size (r)", x = "Impact Factor") +  
    theme_minimal() +
    theme(
        axis.line.y = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 12),
        axis.title = element_text(color = "black", size = 14),
        legend.position = "none"
    )

Figure S16 - Relation between effect size mean values (circles) and journal impact factor. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping.

Supplementary data

All code files and supplementary data used in the study can be found in https://irene-alcocer.github.io/Acoustic-Indices/.

Session info

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
## 
## Matrix products: default
## BLAS:   /usr/lib/libopenblasp-r0.3.10.so
## LAPACK: /usr/lib/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-2 ggplot2_3.3.1      pander_0.6.3       kableExtra_1.1.0  
##  [5] MuMIn_1.43.17      compute.es_0.2-5   metafor_2.4-0      Matrix_1.2-18     
##  [9] stringr_1.4.0      dplyr_1.0.0        png_0.1-7          knitr_1.28        
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.0  xfun_0.14         purrr_0.3.4       splines_4.0.3    
##  [5] lattice_0.20-41   colorspace_1.4-1  vctrs_0.3.0       generics_0.0.2   
##  [9] htmltools_0.4.0   stats4_4.0.3      viridisLite_0.3.0 yaml_2.2.1       
## [13] mgcv_1.8-33       rlang_0.4.6       pillar_1.4.4      glue_1.4.1       
## [17] withr_2.2.0       jpeg_0.1-8.1      lifecycle_0.2.0   munsell_0.5.0    
## [21] gtable_0.3.0      rvest_0.3.5       codetools_0.2-16  evaluate_0.14    
## [25] labeling_0.3      highr_0.8         Rcpp_1.0.4.6      readr_1.3.1      
## [29] scales_1.1.1      webshot_0.5.2     farver_2.0.3      hms_0.5.3        
## [33] digest_0.6.25     stringi_1.4.6     grid_4.0.3        tools_4.0.3      
## [37] magrittr_1.5      tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.3  
## [41] ellipsis_0.3.1    xml2_1.3.2        rmarkdown_2.1     httr_1.4.1       
## [45] rstudioapi_0.11   R6_2.4.1          nlme_3.1-149      compiler_4.0.3

References

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